ZooTask_PreProcessing_12.05.23
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INSTALL NECESSARY PACKAGES:
# install.packages("erp.easy")
library(erp.easy)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(Hmisc)##
## Attaching package: 'Hmisc'## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, unitslibrary(ggplot2, quietly = TRUE, warn.conflicts = FALSE)
library(stringr)
library(tidyr)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsLOCATE FOLDERS:
# Locate the folder for the EEG output files (.txt) for old and new nets, replace the file location below with the one in your local device:
path_newnets <- "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/newnets/"
path_oldnets <- "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/oldnets/"
# Enter the number of participants in each group:
subs_new <- 70
subs_old <- 12LOAD DATA:
- Load the data from all subjects into a dataframe for each of the 4 conditions.
- Convert all variables into factors so that the grand average function can work properly.
- Do this separately for old nets and new nets since we will process them differently based on electrode locations (electrode numbers are different)
- Make sure all files are 250m/s sampling rate - downsample beforehand if needed. Code would not run if a child has higher sampling rate.
# Load data into dataframes for each condition separately (the exported .txt files appear separately for each condition):
neg_go <- load.data(path_newnets,"NegGo", subs_new, -100, 999)
neg_nogo <- load.data(path_newnets,"NegNoGo", subs_new, -100, 999)
neut_go <- load.data(path_newnets,"NeutGo", subs_new, -100, 999)
neut_nogo <- load.data(path_newnets,"NeutNoGo", subs_new, -100, 999)
# Combine all conditions together into a single dataframe:
combo_new <- rbind.data.frame(neg_go, neg_nogo, neut_go, neut_nogo)
combo_new <- as.data.frame(unclass(combo_new), stringsAsFactors=TRUE)
# Repeat for old nets:
neg_go_old <- load.data(path_oldnets,"NegGo", subs_old, -100, 999)
neg_nogo_old <- load.data(path_oldnets,"NegNoGo", subs_old, -100, 999)
neut_go_old <- load.data(path_oldnets,"NeutGo", subs_old, -100, 999)
neut_nogo_old <- load.data(path_oldnets,"NeutNoGo", subs_old, -100, 999)
combo_old <- rbind.data.frame(neg_go_old, neg_nogo_old, neut_go_old, neut_nogo_old)
combo_old <- as.data.frame(unclass(combo_old),stringsAsFactors=TRUE)
head(combo_old)## Subject Stimulus Time V1 V2 V3 V4 V5
## 1 AN122116 NegGo -100 -1.602166 -0.975723 -0.816704 -1.601339 -0.178200
## 2 AN122116 NegGo -96 -1.381717 -0.857681 -0.553844 -0.892159 0.475065
## 3 AN122116 NegGo -92 -0.916006 -0.567245 -0.245930 -0.231857 1.066748
## 4 AN122116 NegGo -88 -0.271272 -0.088409 0.125656 0.340374 1.469912
## 5 AN122116 NegGo -84 0.449695 0.554581 0.589043 0.840698 1.634542
## 6 AN122116 NegGo -80 1.147286 1.300797 1.164803 1.323421 1.615967
## V6 V7 V8 V9 V10 V11 V12
## 1 -0.331926 -0.151961 -0.347968 -0.576614 -1.093344 -1.390728 -1.446274
## 2 0.510233 0.606494 -0.153863 -0.260136 -0.415079 -0.283929 -0.484142
## 3 1.314797 1.333362 0.159202 0.034664 0.071394 0.791458 0.575123
## 4 1.898442 1.850423 0.551349 0.315926 0.373709 1.671227 1.558418
## 5 2.160961 2.056113 0.989324 0.634567 0.603586 2.271284 2.303516
## 6 2.138047 1.978029 1.457013 1.057877 0.921005 2.612692 2.723587
## V13 V14 V15 V16 V17 V18 V19 V20
## 1 0.154436 -0.183043 -0.986050 -1.668789 0.050217 -2.640511 -3.116714 -2.074911
## 2 0.945122 0.372879 -0.317448 -0.819794 0.983765 -1.523602 -1.794190 -1.187153
## 3 1.697449 0.793301 0.149136 0.003761 1.976685 -0.314388 -0.273128 -0.085423
## 4 2.217609 1.043167 0.441242 0.719955 2.786621 0.844208 1.214652 1.044960
## 5 2.406643 1.174946 0.670528 1.307877 3.234940 1.815136 2.451749 1.997532
## 6 2.313116 1.303911 0.981770 1.808807 3.283058 2.517334 3.306489 2.624617
## V21 V22 V23 V24 V25 V26 V27
## 1 -1.612476 -3.177330 -4.650845 -2.473480 -1.961811 -4.129242 -2.788494
## 2 -0.844498 -1.744621 -3.119498 -1.602276 -1.253998 -2.740967 -1.987232
## 3 0.098137 -0.120888 -1.237389 -0.474751 -0.290592 -1.055275 -0.902645
## 4 1.032101 1.405754 0.652441 0.778458 0.766792 0.610847 0.338024
## 5 1.764500 2.557701 2.195314 1.949837 1.698437 1.928261 1.515244
## 6 2.181705 3.180214 3.163316 2.833687 2.327084 2.689941 2.407352
## V28 V29 V30 V31 V32 V33 V34 V35
## 1 -1.600498 0.267609 1.704092 1.655323 1.792316 -1.247337 -1.565639 0.009226
## 2 -0.895760 0.061537 1.575657 1.587364 1.779469 -0.718452 -1.027107 -0.212204
## 3 0.037254 0.093109 1.446121 1.501526 1.689861 0.020538 -0.316589 -0.194126
## 4 1.056332 0.293676 1.282846 1.354988 1.483730 0.877956 0.484066 0.008957
## 5 1.947704 0.504688 1.051548 1.120262 1.165264 1.686760 1.225574 0.244831
## 6 2.520092 0.555014 0.739815 0.802414 0.785526 2.267891 1.750835 0.334261
## V36 V37 V38 V39 V40 V41 V42 V43
## 1 1.743858 2.122482 1.753445 0.243980 -0.360420 2.080679 2.637558 2.488483
## 2 1.622190 1.938988 1.444054 0.475021 -0.622421 1.851799 2.304211 1.770252
## 3 1.530930 1.721893 1.114063 0.813866 -0.621868 1.605459 1.966421 1.092953
## 4 1.412546 1.461838 0.791637 1.169640 -0.392212 1.336784 1.650398 0.551357
## 5 1.209280 1.153973 0.497448 1.426853 -0.084667 1.030151 1.340042 0.177834
## 6 0.894976 0.806694 0.245754 1.489670 0.100655 0.676033 0.992119 -0.057133
## V44 V45 V46 V47 V48 V49 V50
## 1 -0.478058 -0.116197 0.471390 0.894775 1.085449 -0.986816 -0.568535
## 2 -0.332985 -0.481936 -0.240822 0.001236 0.034695 -1.011177 -1.498154
## 3 -0.127296 -0.522299 -0.512642 -0.864632 -0.893692 -0.927132 -2.277191
## 4 0.048119 -0.285610 -0.346947 -1.536613 -1.526505 -0.777245 -2.780609
## 5 0.102623 0.035172 0.028740 -1.958992 -1.841530 -0.652672 -3.026688
## 6 -0.007149 0.196474 0.274559 -2.186066 -1.952347 -0.638664 -3.137680
## V51 V52 V53 V54 V55 V56 V57 V58
## 1 1.070000 1.878197 2.666451 1.737392 1.327618 1.859970 1.614403 2.925186
## 2 -0.160545 0.618912 1.864506 1.494488 1.545944 0.444145 0.366844 1.426971
## 3 -1.294258 -0.504265 1.024616 1.240417 1.603912 -0.640739 -0.757164 -0.111763
## 4 -2.129997 -1.287273 0.292511 0.979656 1.427685 -1.120575 -1.491501 -1.395485
## 5 -2.613663 -1.676357 -0.227856 0.723427 1.046879 -1.017914 -1.773453 -2.266602
## 6 -2.831451 -1.767654 -0.507278 0.491032 0.583645 -0.625381 -1.747795 -2.740128
## V59 V60 V61 V62 V63 V64 V65
## 1 2.046391 1.169464 1.071705 1.248970 1.905968 1.640926 0.002188
## 2 0.602528 0.107323 0.288998 1.150200 0.342133 0.277851 -0.932522
## 3 -0.905182 -0.946693 -0.552670 0.810631 -1.086975 -1.066990 -1.984392
## 4 -2.166298 -1.769859 -1.264359 0.314499 -2.020744 -2.095554 -2.859217
## 5 -2.966969 -2.223263 -1.700798 -0.188576 -2.319824 -2.660908 -3.353342
## 6 -3.268372 -2.299583 -1.814854 -0.550926 -2.108795 -2.801708 -3.424402
## V66 V67 V68 V69 V70 V71 V72
## 1 -0.476954 1.102089 0.719635 1.112701 -0.058893 -1.529216 -0.755066
## 2 -1.551037 0.714247 0.691925 -0.130219 -0.911140 -2.128438 -1.079086
## 3 -2.686024 0.118654 0.379486 -1.340378 -1.866013 -2.868699 -1.558258
## 4 -3.593519 -0.555941 -0.125336 -2.261200 -2.673973 -3.511125 -2.041573
## 5 -4.072910 -1.152937 -0.655533 -2.744957 -3.165016 -3.872424 -2.387306
## 6 -4.085543 -1.551479 -1.043991 -2.798599 -3.303223 -3.889922 -2.522308
## V73 V74 V75 V76 V77 V78 V79 V80
## 1 0.441437 1.033527 0.788539 -0.928551 0.435883 1.081963 2.877953 1.601650
## 2 0.346336 0.178783 0.142629 -1.132133 0.487083 1.028404 2.960096 1.862282
## 3 0.022926 -0.656792 -0.560802 -1.508812 0.197792 0.684425 2.804230 1.968708
## 4 -0.425003 -1.350021 -1.180889 -1.935188 -0.334747 0.144081 2.413523 1.838276
## 5 -0.850845 -1.810688 -1.617779 -2.277023 -0.924843 -0.414665 1.895298 1.485500
## 6 -1.131081 -2.012646 -1.844580 -2.443001 -1.380531 -0.811607 1.405289 1.014156
## V81 V82 V83 V84 V85 V86 V87 V88
## 1 0.848020 0.460829 -1.231041 0.076736 0.138611 2.784137 3.245194 2.589694
## 2 1.362525 -0.121952 -1.526024 0.125122 0.144287 2.785977 3.160503 2.606918
## 3 1.802489 -0.749256 -1.918602 -0.192474 -0.095490 2.586747 2.883027 2.559120
## 4 2.045585 -1.354576 -2.313644 -0.775436 -0.506226 2.155099 2.388915 2.431592
## 5 2.060138 -1.855576 -2.615715 -1.429977 -0.948303 1.556672 1.757413 2.244977
## 6 1.915174 -2.186262 -2.768425 -1.953003 -1.282578 0.921325 1.127977 2.041994
## V89 V90 V91 V92 V93 V94 V95 V96
## 1 -3.432014 -4.049820 -0.531204 2.401985 1.829981 3.451154 -7.335152 -3.743881
## 2 -3.773735 -3.862244 -0.350449 2.178871 1.818653 3.347659 -7.251877 -3.261444
## 3 -4.165321 -3.830048 -0.507690 1.777306 1.638340 3.070870 -7.257523 -3.043457
## 4 -4.501930 -3.879913 -0.965775 1.195198 1.223476 2.590276 -7.266975 -3.139648
## 5 -4.653816 -3.891159 -1.573624 0.498116 0.631874 1.963420 -7.130493 -3.444870
## 6 -4.536186 -3.759476 -2.132874 -0.209702 0.001291 1.298814 -6.726074 -3.756775
## V97 V98 V99 V100 V101 V102 V103 V104
## 1 1.323845 1.687925 1.786144 -4.663246 -2.786003 0.974030 0.679365 1.707811
## 2 1.275421 1.668030 1.726355 -4.882811 -2.492363 0.976803 0.704914 1.570347
## 3 0.907425 1.538475 1.584257 -5.189646 -2.378700 1.009205 0.810230 1.400173
## 4 0.181274 1.197460 1.261652 -5.422186 -2.516228 0.916962 0.854427 1.136353
## 5 -0.772461 0.651726 0.757948 -5.394495 -2.855194 0.627228 0.746681 0.772690
## 6 -1.715271 -0.001589 0.154560 -5.005291 -3.257599 0.161217 0.467112 0.346331
## V105 V106 V107 V108 V109 V110 V111 V112
## 1 3.157804 0.831240 -0.703478 -1.796181 -0.954175 1.336458 1.670211 1.949009
## 2 3.067249 1.364780 -0.046059 -2.326027 -0.743787 1.344398 1.535836 2.193771
## 3 2.822426 1.789238 0.593539 -2.794525 -0.242855 1.456902 1.459781 2.292099
## 4 2.376984 1.999977 1.056548 -2.981224 0.310246 1.582771 1.452214 2.270863
## 5 1.775845 1.987045 1.255971 -2.768326 0.687672 1.625034 1.492644 2.191158
## 6 1.124949 1.833754 1.224303 -2.219368 0.758452 1.505865 1.527507 2.118137
## V113 V114 V115 V116 V117 V118 V119 V120
## 1 -0.539551 -1.150284 -1.752474 -1.822952 1.795980 1.812792 -0.461781 -0.497742
## 2 0.007809 -1.588699 -1.885380 -1.500015 1.551477 1.941059 -0.014542 -0.606674
## 3 0.575939 -1.906067 -1.803888 -0.736660 1.388708 1.938408 0.414474 -0.748016
## 4 1.070267 -1.970215 -1.570978 0.139423 1.359306 1.884426 0.785519 -0.875206
## 5 1.428329 -1.725597 -1.263821 0.797108 1.457943 1.859196 1.088673 -0.915016
## 6 1.653008 -1.227242 -0.942726 1.035713 1.612221 1.904932 1.350513 -0.806335
## V121 V122 V123 V124 V125 V126 V127
## 1 -2.506559 -1.355415 1.636124 -0.138519 -0.575409 -0.063583 -1.093063
## 2 -2.282681 -1.201950 1.676567 0.323805 -0.402016 0.300964 -0.635666
## 3 -1.849167 -0.738174 1.668022 0.681597 -0.205864 0.443298 -0.282318
## 4 -1.320885 -0.027748 1.768433 0.942221 -0.033119 0.364227 -0.125992
## 5 -0.809322 0.793083 2.099190 1.165342 0.105431 0.162506 -0.223267
## 6 -0.376049 1.563683 2.669037 1.429461 0.232117 -0.026382 -0.554886
## V128 V129
## 1 -0.922371 0.064854
## 2 -0.654320 0.651667
## 3 -0.289268 1.148424
## 4 0.054832 1.419500
## 5 0.246422 1.427877
## 6 0.202545 1.258244SPECIFY THE ELECTRODE NUMBERS FOR P2, N2 and P3:
FCz = frontrocentral - midline FC4 = frontrocentral - right FC3 = frontrocentral - left
Pz = parietal - midline P4 = parietal - right P3 = parietal - left
# new nets:
FCz_newnets <- c("V18", "V16", "V10", "V19", "V11", "V4", "V12", "V5", "V6")
FC4_newnets <- c("V3","V123","V124", "V117", "V118")
FC3_newnets <- c("V23","V27","V24", "V28", "V20")
Pz_newnets <- c("V54","V79","V61", "V62","V78", "V67", "V72", "V77")
P4_newnets <- c("V85", "V86", "V91" , "V92" ,"V97", "V98")
P3_newnets <- c("V51", "V52","V53", "V59", "V60", "V47")
# repeat for old nets
FCz_oldnets <- c("V19","V16", "V10", "V20","V11", "V4","V12","V5","V6")
FC4_oldnets <- c("V3","V123","V119", "V123","V118")
FC3_oldnets <- c("V24","V25","V21", "V28","V29")
Pz_oldnets <- c( "V54","V62","V80", "V61","V68","V79", "V67", "V73","V78")
P4_oldnets <- c("V87","V99", "V86", "V93", "V98", "V92")
P3_oldnets <- c("V53","V48", "V52", "V60", "V51", "V59") CHECK OUT INDIVIDUAL WAVEFORMS:
USING MOSAIC YOU CAN CREATE AN AVERAGE WAVEFORM FOR EACH PARTICIPANT IN EACH GROUP & NET LOCATION:
#Create average waveform plots for each subject in a single, multiplot window
mosaic(combo_new, FCz_newnets, cols = 3, rows = 2)
mosaic(combo_new, Pz_newnets, cols = 3, rows = 2)
mosaic(combo_old, FCz_oldnets, cols = 3, rows = 2)
mosaic(combo_old, Pz_oldnets, cols = 3, rows = 2)
CHECK OUT GRAND AVERAGES USING GRANDAVERAGE()
# grand average plots the grand average waveform for each condition present in the dataframe you provide.
#`invisible()` function to hide the data output.
invisible(grandaverage(combo_new, FCz_newnets))
invisible(grandaverage(combo_new, Pz_newnets))
# butterfly plots all individual waveforms for the condition specified by the stim argument(i.e.,a butterfly plot).
# The grandaverage waveform is also plotted,using a red line.
butterfly(combo_new,FCz_newnets, stim=1)
CODE BELOW GETS ALL THE MEASURES (N2, P2, P3) FROM OLD AND NEW NET DATA, COMBINE THEM TOGETHER AND IT SAVES THE DATA INTO A FINAL COMBO SPREADSHEET:
CHECK THE WINDOW RANGE FOR EACH ERP COMPONENT AND ADJUST AS NEEDED!
- The dependent measures we use are mean amplitude (microvolts) and latency (in ms) for statistical analysis.
- m.measures gives the mean amplitude for a specific window and its dt dev. along with its graph. specify lgnd = “n” (no legend) if you do not want the legend
- p.measures calculates local or simple peak amplitude and latency for each condition in the data frame. Use latency only from p.measures
- pol = The polarity of peaks to favor when multiple peaks are present. Entering “pos” will locate the most positive peak. Entering “neg” will locate the most negative peak. Entering “abs” will find the greatest deviation from 0, regardless of the polarity.
P2 between 180–280 ms after stimulus onset at frontal, frontocentral and central electrode sites.
N2 between 320,520 ms after stimulus onset at frontal, frontocentral and central electrode sites.
P3 between 450-750 ms after stimulus onset at parietal electrode sites.
# Get the mean Amplitude measures from the NEW net:
MeanAmp_P2_FCz_newnets <- (m.measures(combo_new, FCz_newnets, window=c(180,280)))
MeanAmp_P2_FC4_newnets <- (m.measures(combo_new, FC4_newnets, window=c(180,280)))
MeanAmp_P2_FC3_newnets <- (m.measures(combo_new, FC3_newnets, window=c(180,280))) 
MeanAmp_N2_FCz_newnets <- (m.measures(combo_new, FCz_newnets, window=c(320,520))) 
MeanAmp_N2_FC4_newnets <- (m.measures(combo_new, FC4_newnets, window=c(320,520)))
MeanAmp_N2_FC3_newnets<- (m.measures(combo_new, FC3_newnets, window=c(320,520))) 
MeanAmp_P3_Pz_newnets <- (m.measures(combo_new, Pz_newnets, window=c(450,750))) 
MeanAmp_P3_P4_newnets <- (m.measures(combo_new, P4_newnets, window=c(450,750))) 
MeanAmp_P3_P3_newnets <- (m.measures(combo_new, P3_newnets, window=c(450,750))) 
# We need to combine these but each one of these datasets use the same variable name - Mean Amplitude.
# Below is a function that will allow us to rename the variables in multiple datasets in a similar way using different names:
rename_datasets_amplitude <- function(dataset_list, new_col_names){
for (i in 1:length(dataset_list)){
assign(dataset_list[i], rename(get(dataset_list[i]),
!!new_col_names[i] := "Mean Amplitude"), envir = .GlobalEnv)
}
}
datasets <- c("MeanAmp_P2_FCz_newnets", "MeanAmp_P2_FC4_newnets", "MeanAmp_P2_FC3_newnets",
"MeanAmp_N2_FCz_newnets", "MeanAmp_N2_FC4_newnets", "MeanAmp_N2_FC3_newnets",
"MeanAmp_P3_Pz_newnets", "MeanAmp_P3_P4_newnets", "MeanAmp_P3_P3_newnets")
new_column_names <- c("MeanAmp_P2_FCz", "MeanAmp_P2_FC4", "MeanAmp_P2_FC3",
"MeanAmp_N2_FCz", "MeanAmp_N2_FC4", "MeanAmp_N2_FC3",
"MeanAmp_P3_Pz", "MeanAmp_P3_P4", "MeanAmp_P3_P3")
rename_datasets_amplitude(datasets, new_column_names)
# load multiple datasets into the workspace
datasets_list <- mget(datasets)
# Using the `Reduce()` function to merge multiple data frames stored in `datasets_list` into a single data frame called `df_merge1`.
# It does this by merging each data frame in the list with the others, based on the columns "Subject" and "Trial Type".
merge_datasets_amplitude <- function(datasets_list) {
Reduce(function(x, y) {
x <- x[, !(names(x) %in% "Standard Dev")] # ignore Standard Dev column from the first dataframe, do not merge it
y <- y[, !(names(y) %in% "Standard Dev")] # ignore Standard Dev column from the next dataframe, do not merge it
merge(x, y, by=c("Subject", "Trial Type"))
}, datasets_list)
}
# Run the function
df_merge1 <- merge_datasets_amplitude(datasets_list) # use the function to merge the new data frames
# Get the Latency measures from the NEW net:
Latency_P2_FCz_newnets <- (p.measures(combo_new, FCz_newnets, window=c(180,280), pol="pos"))
Latency_P2_FC4_newnets <- (p.measures(combo_new, FC4_newnets, window=c(180,280), pol="pos"))
Latency_P2_FC3_newnets <- (p.measures(combo_new, FC3_newnets, window=c(180,280), pol="pos"))
Latency_N2_FCz_newnets <- (p.measures(combo_new, FCz_newnets, window=c(320,520), pol="neg")) 
Latency_N2_FC4_newnets <- (p.measures(combo_new, FC4_newnets, window=c(320,520), pol="neg")) 
Latency_N2_FC3_newnets <- (p.measures(combo_new, FC3_newnets, window=c(320,520), pol="neg")) 
Latency_P3_Pz_newnets <- (p.measures(combo_new, Pz_newnets, window=c(450,750), pol="pos"))
Latency_P3_P4_newnets <- (p.measures(combo_new, P4_newnets, window=c(450,750), pol="pos"))
Latency_P3_P3_newnets <- (p.measures(combo_new, P3_newnets, window=c(450,750), pol="pos"))
# Function `rename_datasets()` that renames the columns "Peak Latency" and "Peak Amplitude" in each data frame from a list of data frames (`dataset_list`)
rename_datasets_latency <- function(dataset_list, new_col_name1, new_col_name2){
for (i in 1:length(dataset_list)){
temp_data <- get(dataset_list[i])
names(temp_data)[names(temp_data) == "Peak Latency"] <- new_col_name1[i]
names(temp_data)[names(temp_data) == "Peak Amplitude"] <- new_col_name2[i]
assign(dataset_list[i], temp_data, envir = .GlobalEnv)
}
}
datasets <- c("Latency_P2_FCz_newnets", "Latency_P2_FC4_newnets", "Latency_P2_FC3_newnets",
"Latency_N2_FCz_newnets", "Latency_N2_FC4_newnets", "Latency_N2_FC3_newnets",
"Latency_P3_Pz_newnets", "Latency_P3_P4_newnets", "Latency_P3_P3_newnets")
new_column_names1 <- c("Latency_P2_FCz", "Latency_P2_FC4", "Latency_P2_FC3",
"Latency_N2_FCz", "Latency_N2_FC4", "Latency_N2_FC3",
"Latency_P3_Pz", "Latency_P3_P4", "Latency_P3_P3")
new_column_names2 <- c("PeakAmp_P2_FCz", "PeakAmp_P2_FC4", "PeakAmp_P2_FC3",
"PeakAmp_N2_FCz", "PeakAmp_N2_FC4", "PeakAmp_N2_FC3",
"PeakAmp_P3_Pz", "PeakAmp_P3_P4", "PeakAmp_P3_P3")
rename_datasets_latency(datasets, new_column_names1, new_column_names2)
# load multiple datasets into the workspace
datasets_list <- mget(datasets)
merge_datasets_latency <- function(datasets_list) {
Reduce(function(x, y) {
merge(x, y, by=c("Subject", "Trial Type"))
}, datasets_list)
}
# Run the function
df_merge2 <- merge_datasets_latency(datasets_list) # use the function to merge the new data frames
# Combine the 2 dataframes
ERP_newnets <- full_join(df_merge1, df_merge2, by = c("Subject", "Trial Type"))
head(ERP_newnets)## Subject Trial Type MeanAmp_P2_FCz MeanAmp_P2_FC4 MeanAmp_P2_FC3
## 1 AE050318 NegGo -8.6567894 -10.997870 -8.274136
## 2 AE050318 NegNoGo 0.1368089 3.073859 -10.233068
## 3 AE050318 NeutGo -11.0096655 -10.983267 -8.061495
## 4 AE050318 NeutNoGo -8.0131387 -7.947222 -8.404834
## 5 AH101121 NegGo -6.6852204 -9.178219 -7.760717
## 6 AH101121 NegNoGo -2.2374154 -4.678083 -4.003878
## MeanAmp_N2_FCz MeanAmp_N2_FC4 MeanAmp_N2_FC3 MeanAmp_P3_Pz MeanAmp_P3_P4
## 1 -17.640501 -17.0612959 -12.45291 4.369997 -1.0128204
## 2 -2.763603 -0.6563204 -11.78551 3.345500 0.4492580
## 3 -18.544452 -17.9227477 -12.32419 4.253425 -0.6663896
## 4 -14.488076 -16.2244929 -12.51434 6.818025 -1.6119402
## 5 -22.625932 -24.2391735 -23.10149 18.547604 11.2572051
## 6 -22.243306 -20.8193210 -24.12986 14.173249 11.9982319
## MeanAmp_P3_P3 Latency_P2_FCz PeakAmp_P2_FCz Latency_P2_FC4 PeakAmp_P2_FC4
## 1 7.006061 212 -6.2425933 208 -8.224361
## 2 2.221935 252 3.3000709 232 5.512485
## 3 6.126728 220 -8.4652572 220 -9.281374
## 4 12.549254 216 -4.2941628 216 -3.940464
## 5 16.239133 228 -5.6526816 188 -7.083663
## 6 5.765768 240 -0.5854749 204 -1.564159
## Latency_P2_FC3 PeakAmp_P2_FC3 Latency_N2_FCz PeakAmp_N2_FCz Latency_N2_FC4
## 1 200 -6.982536 352 -23.058663 352
## 2 188 -8.637317 452 -5.257907 400
## 3 212 -6.498847 384 -22.615500 384
## 4 180 -6.557909 320 -20.985998 320
## 5 236 -6.305076 492 -25.080314 484
## 6 240 -1.848016 396 -24.552156 384
## PeakAmp_N2_FC4 Latency_N2_FC3 PeakAmp_N2_FC3 Latency_P3_Pz PeakAmp_P3_Pz
## 1 -20.626624 368 -17.04337 612 6.037603
## 2 -3.018455 408 -12.41742 600 6.301873
## 3 -21.637845 388 -14.78690 592 6.610696
## 4 -21.846657 324 -16.75670 640 10.395567
## 5 -27.616219 520 -26.08764 528 23.899982
## 6 -27.499941 472 -26.48135 508 18.808306
## Latency_P3_P4 PeakAmp_P3_P4 Latency_P3_P3 PeakAmp_P3_P3
## 1 576 0.8441270 512 9.862223
## 2 508 3.1160960 648 5.847591
## 3 488 0.3001207 516 8.529244
## 4 508 0.4807528 524 14.750284
## 5 536 15.0651712 512 20.561269
## 6 584 16.8901785 652 4.791160# remove standard dev column:
# ERP_newnets <- ERP_newnets %>% select(-`Standard Dev`)REPEAT FOR OLD NETS!
MeanAmp_P2_FCz_oldnets <- (m.measures(combo_old, FCz_oldnets, window=c(180,280)))
MeanAmp_P2_FC4_oldnets <- (m.measures(combo_old, FC4_oldnets, window=c(180,280)))
MeanAmp_P2_FC3_oldnets <- (m.measures(combo_old, FC3_oldnets, window=c(180,280)))
MeanAmp_N2_FCz_oldnets <- (m.measures(combo_old, FCz_oldnets, window=c(320,520))) 
MeanAmp_N2_FC4_oldnets <- (m.measures(combo_old, FC4_oldnets, window=c(320,520))) 
MeanAmp_N2_FC3_oldnets <- (m.measures(combo_old, FC3_oldnets, window=c(320,520))) 
MeanAmp_P3_Pz_oldnets <- (m.measures(combo_old, Pz_oldnets, window=c(450,750))) 
MeanAmp_P3_P4_oldnets <- (m.measures(combo_old, P4_oldnets, window=c(450,750))) 
MeanAmp_P3_P3_oldnets <- (m.measures(combo_old, P3_oldnets, window=c(450,750))) 
datasets <- c("MeanAmp_P2_FCz_oldnets", "MeanAmp_P2_FC4_oldnets", "MeanAmp_P2_FC3_oldnets",
"MeanAmp_N2_FCz_oldnets", "MeanAmp_N2_FC4_oldnets", "MeanAmp_N2_FC3_oldnets",
"MeanAmp_P3_Pz_oldnets", "MeanAmp_P3_P4_oldnets", "MeanAmp_P3_P3_oldnets")
new_column_names <- c("MeanAmp_P2_FCz", "MeanAmp_P2_FC4", "MeanAmp_P2_FC3",
"MeanAmp_N2_FCz", "MeanAmp_N2_FC4", "MeanAmp_N2_FC3",
"MeanAmp_P3_Pz", "MeanAmp_P3_P4", "MeanAmp_P3_P3")
rename_datasets_amplitude(datasets, new_column_names)
datasets_list <- mget(datasets)
df_merge1_old <- merge_datasets_amplitude(datasets_list) # use the function to merge the new data frames
Latency_P2_FCz_oldnets <- (p.measures(combo_old, FCz_oldnets, window=c(180,280), pol="pos"))
Latency_P2_FC4_oldnets <- (p.measures(combo_old, FC4_oldnets, window=c(180,280), pol="pos"))
Latency_P2_FC3_oldnets <- (p.measures(combo_old, FC3_oldnets, window=c(180,280), pol="pos"))
Latency_N2_FCz_oldnets <- (p.measures(combo_old, FCz_oldnets, window=c(320,520), pol="neg")) 
Latency_N2_FC4_oldnets <- (p.measures(combo_old, FC4_oldnets, window=c(320,520), pol="neg")) 
Latency_N2_FC3_oldnets <- (p.measures(combo_old, FC3_oldnets, window=c(320,520), pol="neg")) 
Latency_P3_Pz_oldnets <- (p.measures(combo_old, Pz_oldnets, window=c(450,750), pol="pos"))
Latency_P3_P4_oldnets <- (p.measures(combo_old, P4_oldnets, window=c(450,750), pol="pos"))
Latency_P3_P3_oldnets <- (p.measures(combo_old, P3_oldnets, window=c(450,750), pol="pos"))
datasets <- c("Latency_P2_FCz_oldnets", "Latency_P2_FC4_oldnets", "Latency_P2_FC3_oldnets",
"Latency_N2_FCz_oldnets", "Latency_N2_FC4_oldnets", "Latency_N2_FC3_oldnets",
"Latency_P3_Pz_oldnets", "Latency_P3_P4_oldnets", "Latency_P3_P3_oldnets")
new_column_names1 <- c("Latency_P2_FCz", "Latency_P2_FC4", "Latency_P2_FC3",
"Latency_N2_FCz", "Latency_N2_FC4", "Latency_N2_FC3",
"Latency_P3_Pz", "Latency_P3_P4", "Latency_P3_P3")
new_column_names2 <- c("PeakAmp_P2_FCz", "PeakAmp_P2_FC4", "PeakAmp_P2_FC3",
"PeakAmp_N2_FCz", "PeakAmp_N2_FC4", "PeakAmp_N2_FC3",
"PeakAmp_P3_Pz", "PeakAmp_P3_P4", "PeakAmp_P3_P3")
rename_datasets_latency(datasets, new_column_names1, new_column_names2)
datasets_list <- mget(datasets)
df_merge2_old <- merge_datasets_latency(datasets_list) # use the function to merge the new data frames
# Combine the 2 dataframes
ERP_oldnets <- full_join(df_merge1_old, df_merge2_old, by = c("Subject", "Trial Type"))
head(ERP_oldnets)## Subject Trial Type MeanAmp_P2_FCz MeanAmp_P2_FC4 MeanAmp_P2_FC3
## 1 AN122116 NegGo -4.1407086 -1.4327522 -5.0320122
## 2 AN122116 NegNoGo -11.0193942 -0.1925687 -0.2935563
## 3 AN122116 NeutGo -5.1375479 -13.6733601 0.6677448
## 4 AN122116 NeutNoGo -7.0921285 -5.8957279 7.5732563
## 5 AS110816 NegGo -6.7244279 -8.5284981 -6.5214486
## 6 AS110816 NegNoGo 0.9807348 -1.9055810 -2.6025719
## MeanAmp_N2_FCz MeanAmp_N2_FC4 MeanAmp_N2_FC3 MeanAmp_P3_Pz MeanAmp_P3_P4
## 1 -8.29314 -1.460082 -10.78825393 2.783801 4.021132
## 2 -20.15702 -1.218012 -13.32166345 5.369831 1.153689
## 3 -13.74024 -17.345666 -6.70710155 9.747895 14.534170
## 4 -16.52584 -16.467716 0.01039033 5.414394 7.557088
## 5 -17.48872 -14.803429 -16.30025058 5.850186 14.521829
## 6 -11.91471 -13.806399 -15.03030459 2.999593 7.822631
## MeanAmp_P3_P3 Latency_P2_FCz PeakAmp_P2_FCz Latency_P2_FC4 PeakAmp_P2_FC4
## 1 -5.3793184 196 -1.697444 212 2.326486
## 2 -0.3086019 252 -9.920439 256 5.021367
## 3 -1.0471026 212 -1.309128 212 -9.682326
## 4 8.0758937 196 -5.072943 192 -1.011759
## 5 5.8252172 256 -5.428295 252 -7.055694
## 6 -6.5985243 224 3.456288 236 1.125501
## Latency_P2_FC3 PeakAmp_P2_FC3 Latency_N2_FCz PeakAmp_N2_FCz Latency_N2_FC4
## 1 244 -3.0044314 436 -10.42853 440
## 2 180 7.5681562 376 -25.68119 384
## 3 232 2.9387006 484 -16.53051 380
## 4 244 11.5706090 488 -20.65452 380
## 5 252 -5.2547888 368 -21.73219 360
## 6 224 -0.7118974 352 -17.74692 352
## PeakAmp_N2_FC4 Latency_N2_FC3 PeakAmp_N2_FC3 Latency_P3_Pz PeakAmp_P3_Pz
## 1 -4.376464 420 -13.546851 520 6.711619
## 2 -9.214241 504 -19.674420 544 13.155540
## 3 -20.075206 464 -8.761441 612 15.601991
## 4 -20.232107 404 -4.058091 708 10.309167
## 5 -18.829883 372 -21.274613 500 12.045843
## 6 -22.411000 348 -20.104096 512 6.997815
## Latency_P3_P4 PeakAmp_P3_P4 Latency_P3_P3 PeakAmp_P3_P3
## 1 472 7.754448 460 3.051898
## 2 544 7.397239 468 20.779675
## 3 700 16.257373 488 6.043558
## 4 520 12.118001 688 13.128550
## 5 460 23.680317 524 8.128814
## 6 588 10.384612 480 -1.250488#COMBINE OLD AND NEW DATASETS #CREATE A NEW VARIABLE CALLED N2-P2 DIFFERENCE #RE-SHAPE THE DATA #SAVE INTO A CSV FILE
# Combine old +new
ERP <- full_join(ERP_oldnets, ERP_newnets)## Joining with `by = join_by(Subject, `Trial Type`, MeanAmp_P2_FCz,
## MeanAmp_P2_FC4, MeanAmp_P2_FC3, MeanAmp_N2_FCz, MeanAmp_N2_FC4, MeanAmp_N2_FC3,
## MeanAmp_P3_Pz, MeanAmp_P3_P4, MeanAmp_P3_P3, Latency_P2_FCz, PeakAmp_P2_FCz,
## Latency_P2_FC4, PeakAmp_P2_FC4, Latency_P2_FC3, PeakAmp_P2_FC3, Latency_N2_FCz,
## PeakAmp_N2_FCz, Latency_N2_FC4, PeakAmp_N2_FC4, Latency_N2_FC3, PeakAmp_N2_FC3,
## Latency_P3_Pz, PeakAmp_P3_Pz, Latency_P3_P4, PeakAmp_P3_P4, Latency_P3_P3,
## PeakAmp_P3_P3)`# Remove Grand Ave from data, order by subject name and reset the index:
ERP <- ERP[!(ERP$Subject=="Grand Avg"),]
ERP <- with(ERP, ERP[order(Subject) , ])
rownames(ERP) <- NULL # Reset index
# CREATE A NEW COLUMN by taking the difference between N2-P2
ERP$MeanAmp_N2P2_FCz <- ERP$MeanAmp_N2_FCz - ERP$MeanAmp_P2_FCz
ERP$PeakAmp_N2P2_FCz <- ERP$PeakAmp_N2_FCz - ERP$PeakAmp_P2_FCz
ERP$MeanAmp_N2P2_FC4 <- ERP$MeanAmp_N2_FC4 - ERP$MeanAmp_P2_FC4
ERP$PeakAmp_N2P2_FC4 <- ERP$PeakAmp_N2_FC4 - ERP$PeakAmp_P2_FC4
ERP$MeanAmp_N2P2_FC3 <- ERP$MeanAmp_N2_FC3 - ERP$MeanAmp_P2_FC3
ERP$PeakAmp_N2P2_FC3 <- ERP$PeakAmp_N2_FC3 - ERP$PeakAmp_P2_FC3
# REORGANIZE STIMTAG VARIABLE AS TWO SEPERATE VARIABLES: EMOTION AND CONDITION - EACH WITH TWO LEVELS
ERP <- ERP %>%
mutate(Emotion = str_extract(`Trial Type`, "Neut|Neg"),
Condition = str_extract(`Trial Type`, "Go|NoGo"))
# RESHAPE TO LONG FORMAT
ERP_long <- pivot_longer(ERP,
cols = -c(Subject, `Trial Type`, `Condition`, `Emotion`),
names_to = c(".value", "Electrode"),
names_pattern = "^(MeanAmp_P2|MeanAmp_N2|MeanAmp_N2P2|MeanAmp_P3|PeakAmp_P2|PeakAmp_N2|PeakAmp_N2P2|PeakAmp_P3|Latency_P2|Latency_N2|Latency_P3)_(.+)$",
values_to = "Value"
) %>%
mutate(
#Region = ifelse(grepl("^FC", Electrode), "Frontocentral", "Parietal"),
Laterality = ifelse(grepl("4", Electrode), "Right",
ifelse(grepl("3", Electrode), "Left", "Midline"))
)
# select(-Electrode)
# MAKE IT COMPACT
ERP_long <- ERP_long %>%
group_by(Subject, `Trial Type`, Emotion, Condition, Laterality) %>%
summarise(across(everything(), ~mean(., na.rm = TRUE)), .groups = 'drop')## Warning: There were 984 warnings in `summarise()`.
## The first warning was:
## ℹ In argument: `across(everything(), ~mean(., na.rm = TRUE))`.
## ℹ In group 1: `Subject = "AE050318"`, `Trial Type = "NegGo"`, `Emotion =
## "Neg"`, `Condition = "Go"`, `Laterality = "Left"`.
## Caused by warning in `mean.default()`:
## ! argument is not numeric or logical: returning NA
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 983 remaining warnings.head(ERP_long)## # A tibble: 6 × 17
## Subject `Trial Type` Emotion Condition Laterality Electrode MeanAmp_P2
## <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 AE050318 NegGo Neg Go Left NA -8.27
## 2 AE050318 NegGo Neg Go Midline NA -8.66
## 3 AE050318 NegGo Neg Go Right NA -11.0
## 4 AE050318 NegNoGo Neg NoGo Left NA -10.2
## 5 AE050318 NegNoGo Neg NoGo Midline NA 0.137
## 6 AE050318 NegNoGo Neg NoGo Right NA 3.07
## # ℹ 10 more variables: MeanAmp_N2 <dbl>, MeanAmp_P3 <dbl>, Latency_P2 <dbl>,
## # PeakAmp_P2 <dbl>, Latency_N2 <dbl>, PeakAmp_N2 <dbl>, Latency_P3 <dbl>,
## # PeakAmp_P3 <dbl>, MeanAmp_N2P2 <dbl>, PeakAmp_N2P2 <dbl># Write to a csv file:
write.csv(ERP_long, "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/ERP_long")LOAD AND MERGE WITH INTAKE (DEMOGRAPHICS AND CLINICAL ASSESSMENTS) INFO:
- Load data exported from RedCap, named: Intake_Stuttering_Language_Varbls_for_Zoo_and_Reactivity
- Feature engineer necessary variables and merge the above ERP dataset with TalkerGroup, Gender, Age info along with Stuttering and Language Scores
# Load DataSet:
intake <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CognitiveEmotionalLi-IntakeDiagnosticData_DATA_2023-10-25_0818.csv')
# Subject IDs include the visit number in the combo dataset if it is NOT the first time point.
# Do the same here: Combine visit number with subject and create a new Subject variable so that it matches the combo:
intake <- intake %>%
mutate(Subject = ifelse(redcap_event_name !="t1_arm_1", paste0(part_id_status, "T", visitnumber), part_id_status))
# Create a new variable representing final sldper100words ("disfluency_sldper100words_final) by taking disfluency_sldper100words from CVD as primary,
# but in the case that this data is missing, take the disfluency scores from CVE:
intake <- intake %>%
mutate(disfluency_sldper100words_final = ifelse(!is.na(disfluency_sldper100words), disfluency_sldper100words, disfluency_sldper100words_cve))
# Create a final talker group variable ("talkergroup_final) using disfluency_sldper100words_final and talker group based on parent report:
# 1: CWS, 0:CWNS, 9:unidentified
intake <- intake %>%
mutate(talkergroup_final = ifelse((disfluency_sldper100words_final >= 3 | calculator_talkergroup_parent == 1), 1,
ifelse((disfluency_sldper100words_final < 3 & calculator_talkergroup_parent == 0), 0, 9)))
# Take the relevant columns from intake dataset
# You may update this to take more columns into the dataset:
intake <- subset(intake, select=c('Subject','calculator_age_cve','calculator_gender_cve','race', 'ethnicity',
'calculator_talkergroup_parent','tso_calculated',
'disfluency_sldper100words','ssi_total',
'disfluency_sldper100words_final', 'talkergroup_final',
"gfta_standard", "ppvt_standard", "evt_standard",
'teld_rec_standard','teld_exp_standard', "teld_spokenlang_standard",
'tocs_1_total', 'tocs_2_total', 'tcs_total',
'eprime_condorder_zootask','cve_comments','comments_tasks','handedness_zoo'))
# Merge with the main dataset using SUBJECT
FULL <- merge(ERP_long, intake, by=c("Subject"), all.x = TRUE)
head(FULL) ## Subject Trial Type Emotion Condition Laterality Electrode MeanAmp_P2
## 1 AE050318 NegGo Neg Go Left NA -8.2741357
## 2 AE050318 NegGo Neg Go Midline NA -8.6567894
## 3 AE050318 NegGo Neg Go Right NA -10.9978696
## 4 AE050318 NegNoGo Neg NoGo Left NA -10.2330683
## 5 AE050318 NegNoGo Neg NoGo Midline NA 0.1368089
## 6 AE050318 NegNoGo Neg NoGo Right NA 3.0738593
## MeanAmp_N2 MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3
## 1 -12.4529149 7.006061 200 -6.982536 368 -17.043367 512
## 2 -17.6405009 4.369997 212 -6.242593 352 -23.058663 612
## 3 -17.0612959 -1.012820 208 -8.224361 352 -20.626624 576
## 4 -11.7855128 2.221935 188 -8.637317 408 -12.417423 648
## 5 -2.7636031 3.345500 252 3.300071 452 -5.257907 600
## 6 -0.6563204 0.449258 232 5.512485 400 -3.018455 508
## PeakAmp_P3 MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve calculator_gender_cve
## 1 9.862223 -4.178779 -10.060831 38.1 0
## 2 6.037603 -8.983711 -16.816070 38.1 0
## 3 0.844127 -6.063426 -12.402263 38.1 0
## 4 5.847591 -1.552445 -3.780106 38.1 0
## 5 6.301873 -2.900412 -8.557978 38.1 0
## 6 3.116096 -3.730180 -8.530940 38.1 0
## race ethnicity calculator_talkergroup_parent tso_calculated
## 1 2 0 1 1.9
## 2 2 0 1 1.9
## 3 2 0 1 1.9
## 4 2 0 1 1.9
## 5 2 0 1 1.9
## 6 2 0 1 1.9
## disfluency_sldper100words ssi_total disfluency_sldper100words_final
## 1 12 23 12
## 2 12 23 12
## 3 12 23 12
## 4 12 23 12
## 5 12 23 12
## 6 12 23 12
## talkergroup_final gfta_standard ppvt_standard evt_standard teld_rec_standard
## 1 1 121 126 123 146
## 2 1 121 126 123 146
## 3 1 121 126 123 146
## 4 1 121 126 123 146
## 5 1 121 126 123 146
## 6 1 121 126 123 146
## teld_exp_standard teld_spokenlang_standard tocs_1_total tocs_2_total
## 1 135 149 22 6
## 2 135 149 22 6
## 3 135 149 22 6
## 4 135 149 22 6
## 5 135 149 22 6
## 6 135 149 22 6
## tcs_total eprime_condorder_zootask cve_comments comments_tasks handedness_zoo
## 1 25 1 NA
## 2 25 1 NA
## 3 25 1 NA
## 4 25 1 NA
## 5 25 1 NA
## 6 25 1 NA# Print the unique subject codes
unique_codes<- unique(FULL$Subject)
unique_codes <- as.data.frame(unique_codes)
unique_codes## unique_codes
## 1 AE050318
## 2 AH101121
## 3 AK022218
## 4 AK102221
## 5 AL041819
## 6 AN122116
## 7 AS110816
## 8 AT051818
## 9 AW040217
## 10 AW110418
## 11 BW071018T2
## 12 CC031323
## 13 CC102318T2
## 14 CF101019
## 15 CL040218
## 16 CM101322
## 17 CM120919
## 18 DJ052417
## 19 EC041817
## 20 EG030618
## 21 EM100417
## 22 ES031519
## 23 ES032018
## 24 FM032823
## 25 FW121816
## 26 GB012717
## 27 GR091721T2
## 28 HC102117
## 29 HC111621
## 30 HH061919
## 31 HW110822
## 32 JA092118
## 33 JG091119T3
## 34 JJ011018
## 35 JK032119T3
## 36 JM041823
## 37 JS121321
## 38 JT051618
## 39 KA022017
## 40 KE050718
## 41 KT072319
## 42 LB012619
## 43 LB111121
## 44 LG100721
## 45 LO042723
## 46 LT112916
## 47 LW102219T3
## 48 MF101019
## 49 MM040119
## 50 MR091118T2
## 51 MS102319
## 52 NL041119
## 53 OB032423
## 54 OG013016
## 55 OJ032223
## 56 PB021519
## 57 PB031723
## 58 PW030417
## 59 PW041023
## 60 RB041423
## 61 RB101619
## 62 RC102022
## 63 RH100218
## 64 RK040219T3
## 65 RO042723
## 66 RS030518
## 67 RT032219
## 68 RT042523
## 69 SB111121
## 70 SC051023
## 71 SK041519
## 72 SL090418
## 73 SP010219
## 74 ST100121T2
## 75 TA051917
## 76 TE062818
## 77 TS011518
## 78 WB110221
## 79 WF080417
## 80 WH022219
## 81 WK011122
## 82 WS051018# Making sure the Final FULL dataset has the all subjects coded (initially specified by SUBS - subject number):
subs = 82
(nrow(FULL)/12) == subs # This should give TRUE! 12 rows per subject## [1] TRUEMANUALLY ADD TALKER GROUP IS MISSING
# Show the rows where talkergroup_final = 9 or NA :
undefined_talkergroup <- subset(FULL, talkergroup_final == 9 | is.na(talkergroup_final))
print(unique(undefined_talkergroup$Subject))## character(0)# FIND THE UNDEFINED (9) TALKER GROUPS AND MANUALLY MARK THEM AS EITHER 1 or 0 if needed:
# Replace NA values in a specific column based on a condition:
# FULL$talkergroup_final <- ifelse(FULL$Subject == "JA092118", 0, FULL$talkergroup_final)
# FULL$talkergroup_final <- ifelse(FULL$Subject == "LG100721T2", 1, FULL$talkergroup_final)
# FULL$talkergroup_final <- ifelse(FULL$Subject == "LW102219T3", 1, FULL$talkergroup_final)
# Making sure no 9 or NA remained:
any(FULL$talkergroup_final == 9 | is.na(FULL$talkergroup_final))## [1] FALSE#PRINT THE NUMBER OF KIDS IN EACH GROUP
# How many kids in each group?
talkergroup_counts <- table(FULL$talkergroup_final)
print(talkergroup_counts)/12##
## 0 1
## 504 480##
## 0 1
## 42 40ADD THE AVAILABLE LONGITUDINAL CATEGORIES IF AVAILABLE:
# Load DataSet:
# longitudinal_groups <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/LongitudinalClassifications_9.10.23.csv')
# Merge with the main dataset using SUBJECT
# FULL <- merge(FULL, longitudinal_groups[, c("Subject", "Longitudinal_Group_0Rec_1Per_2Und")], by=c("Subject"), all.x = TRUE)
# Making sure the Final FULL dataset has the all subjects coded (initially specified by SUBS - subject number):
# (nrow(FULL)/4) == subs # This should give TRUE!
# How many kids in each group?
# longtgroup_counts <- table(FULL$Longitudinal_Group_0Rec_1Per_2Und)
# print(longtgroup_counts)/4LOAD AND MERGE WITH BEHAVIORAL DATA:
- for GO and NOGO conditions use ShowStim.RESP to compute the accuracy.
- ShowStim.RESP: 4 it means the child pushed the button (accurate for Go), NA means no response (accurate for NoGo).
Trials with responses faster than 200 ms were eliminated from the analysis, as they were too quick to reflect responding to the current stimulus.
For each participant, the frequency of the following variables was recorded and stored: (a) ‘hits - GO’ (when a Go-stimulus was followed by a response falling between 200 and 2300 ms after stimulus onset), (b) ‘hits - NOGO’ (when a NoGo-stimulus was NOT followed by a response, (c) ‘misses - GO’ (when a Go stimulus was not followed by a response), (d) ‘misses - NOGO - false alarm’ (when a NoGo stimuluswas followed by pressing the response button between 200 and 2300 ms after stimulus onset) (e-f) ‘premature responses - GO and NoGo’ (when the response button was pressed between 0 and 200 ms after stimulus onset). (g) ‘sensitivity’ d’: the z-transformed hit rate minus the z-transformed false alarm rate (Macmillan & Creelman, 2004). A d’ of zero indicates no ability to respond discriminately to the go and no-go stimuli. d = z(H) - z(FA), where z(H) and z(FA) represent the transformation of the hit (correct go trials) and false alarm (commission error) rates to z-scores. The variable d’ represents a measure of the perceptual sensitivity to different stimulus conditions, indicating how well participants can discriminate and appropriately respond to targets and non-targets, thus further inspecting cognitive control [56, 57].
Sensitivity scores, calculated using the formula d=z(H)−z(FA), provide a more detailed and nuanced understanding of the participant’s performance in a Go-NoGo task, taking into account both hits and false alarms and providing insight into the ability to discriminate between Go and NoGo stimuli.
Inhibitory Control and Discrimination Ability:Sensitivity scores provide a more specific measure of the ability to discriminate between Go and NoGo trials.A higher sensitivity score indicates better discrimination ability and better inhibitory control.
Trade-off between Hits and False Alarms: Sensitivity takes into account both correct responses to Go trials (hits) and incorrect responses to NoGo trials (false alarms). It helps you understand the balance or trade-off between the child’s ability to detect Go stimuli and their ability to withhold responses to NoGo stimuli.
Insight into Performance Patterns: A sensitivity score provides a more comprehensive view of performance patterns. For example, a high Go accuracy (90%) might suggest good attention, but a low NoGo accuracy (60%) could indicate a potential issue with inhibitory control. Sensitivity scores help integrate these aspects into a single measure.
Statistical Normalization:By transforming hit and false alarm rates into z-scores, sensitivity scores allow for statistical normalization. This can be particularly useful when comparing performance across different groups or conditions.
For each participant, the frequency of the following variables was automatically recorded and stored: (a) ‘hits’ (when a Go-stimulus was followed by a response falling between 200 and 2300 ms after stimulus onset), (b) ‘misses’ (when a Go- stimulus was not followed by a response), (c) ‘false alarms’ (when a NoGo-stimulus was followed by pressing the response button between 200 and 2300 ms after stimulus onset), and (d) ‘premature responses’ (when the response button was pressed between 0 and 200 ms after stimulus onset).
# Load the file:
accuracy <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/Eprime_01.09.24.csv')
# Take only the relevant variables:
accuracy <- subset(accuracy, select=c('Name','VisitNumber','ShowStim.ACC','ShowStim.RESP','ShowStim.RT','StimTag'))
# REORGANIZE STIMTAG VARIABLE AS TWO SEPERATE VARIABLES: EMOTION AND CONDITION - EACH WITH TWO LEVELS
accuracy <- accuracy %>%
mutate(
Emotion = str_extract(StimTag, "neu|neg"),
Condition = str_extract(StimTag, "G|N"),
Emotion = case_when(
Emotion == "neu" ~ "Neut",
Emotion == "neg" ~ "Neg",
TRUE ~ Emotion
),
Condition = case_when(
Condition == "G" ~ "Go",
Condition == "N" ~ "NoGo",
TRUE ~ Condition
)
)
# Convert all empty strings to NA
accuracy <- replace(accuracy, accuracy == "", NA)
# accuracy$ShowStim.RESP <- na_if(accuracy$ShowStim.RESP, "")
# Combine visit number with subject and create a new Subject variable for eprime so that it matches the FULL
accuracy <- accuracy %>%
mutate(Subject = ifelse(VisitNumber != 1, paste0(Name, "T", VisitNumber), Name))
# Print unique subjects in FULL
unique_subjects <- unique(FULL$Subject)
# Filter 'accuracy' dataframe by using the Subjects in FULL dataset only
accuracy <- accuracy[accuracy$`Subject` %in% unique_subjects, ]
# Show the unique counts for ShowStim.RESP - which buttons were pressed - number 4 is the proper response button
unique_codes <- unique(accuracy$ShowStim.RESP)
counts <- as.data.frame(table(accuracy$ShowStim.RESP))
counts## Var1 Freq
## 1 {ALT} 1
## 2 {CONTROL} 8
## 3 {SHIFT} 1
## 4 {SPACE} 3
## 5 1 5
## 6 2 2
## 7 3 53
## 8 4 19980
## 9 6 11# Check out the class types for each variable.
sapply(accuracy, class)## Name VisitNumber ShowStim.ACC ShowStim.RESP ShowStim.RT
## "character" "integer" "integer" "character" "integer"
## StimTag Emotion Condition Subject
## "character" "character" "character" "character"# For ShowStim.RESP response 4 is a "character", not integer.
print(class(accuracy$ShowStim.RESP))## [1] "character"# Convert character 4 for ShowStim.RESP to integer
# accuracy$ShowStim.RESP <- as.integer(accuracy$ShowStim.RESP)
# Remove the special character trials, and only keep those trials with proper number responses.
unique(accuracy$ShowStim.RESP)## [1] "4" NA "1" "{SPACE}" "3" "{CONTROL}"
## [7] "{SHIFT}" "2" "{ALT}" "6"accuracy <- accuracy[is.na(accuracy$ShowStim.RESP) | accuracy$ShowStim.RESP %in% c(1,2,3,4,5,6) , ]
# Drop the 2 rows with NA in StimTag:
accuracy <- accuracy[!is.na(accuracy$StimTag), ] CREATE THE NEW VARIABLES:
# Our max RT is 2023 SO no need to limit it.
accuracy <- accuracy %>%
mutate(behavior = case_when(
(ShowStim.RT >= 200 & ShowStim.RESP == 4) & (StimTag %in% c("negG", "neuG")) ~ "hit_go",
(ShowStim.RT == 0) & (StimTag %in% c("negG", "neuG")) ~ "miss_go",
(ShowStim.RT == 0 & (StimTag %in% c("negN", "neuN"))) ~ "hit_nogo",
(ShowStim.RT >= 200 & (ShowStim.RESP %in% 1:6) & (StimTag %in% c("negN", "neuN"))) ~ "miss_nogo_falsealarm",
(ShowStim.RT < 200 & ShowStim.RT > 0 & ShowStim.RESP %in% 1:6) & (StimTag %in% c("negG", "neuG")) ~ "premature_go",
(ShowStim.RT < 200 & ShowStim.RT > 0 & ShowStim.RESP %in% 1:6) & (StimTag %in% c("negN", "neuN")) ~ "premature_nogo",
TRUE ~ NA_character_ # is a catch-all condition that assigns `NA` to any rows that don't meet any of the previous conditions
))CALCULATE THE PROPORTIONS:
behavioral_measures <- accuracy %>%
group_by(Subject, Condition, Emotion) %>%
summarise(
correct_go = sum(behavior == "hit_go", na.rm = TRUE),
incorrect_go = sum(behavior == "miss_go", na.rm = TRUE),
premature_go = sum(behavior == "premature_go", na.rm = TRUE),
total_go = (correct_go + incorrect_go + premature_go),
accuracy_go_proportion = (correct_go / total_go) *100,
hit_rate = accuracy_go_proportion/100,
premature_go_proportion = (premature_go / (correct_go + premature_go))*100,
correct_nogo = sum(behavior == "hit_nogo", na.rm = TRUE),
incorrect_nogo = sum(behavior == "miss_nogo_falsealarm", na.rm = TRUE),
premature_nogo = sum(behavior == "premature_nogo", na.rm = TRUE),
total_nogo = (correct_nogo + incorrect_nogo + premature_nogo),
accuracy_nogo_proportion = (correct_nogo / total_nogo) *100,
false_alarm_proportion = (incorrect_nogo / total_nogo) *100,
false_alarm_rate = false_alarm_proportion/100,
premature_nogo_proportion = ifelse(incorrect_nogo == 0 & premature_nogo == 0, 0, 100 * premature_nogo / max(incorrect_nogo + premature_nogo, 1)), #In this formula, the `ifelse()` function checks if the sum of `incorrect_nogo` and `premature_nogo` is equal to 0. If it is, `premature_nogo_proportion` is set to 0. If the sum is not equal to 0, the original formula is used to calculate `premature_nogo_proportion. By using the max function with the constant 1, the formula guarantees a minimum denominator of 1, preventing division by zero errors.
# Calculate reaction times:
RT_proper_go = mean(ShowStim.RT[behavior == "hit_go"], na.rm = TRUE),
RT_proper_nogo = mean(ShowStim.RT[behavior == "miss_nogo_falsealarm"], na.rm = TRUE),
RT_premature_go = mean(ShowStim.RT[behavior == "premature_go"], na.rm = TRUE),
RT_premature_nogo = mean(ShowStim.RT[behavior == "premature_nogo"], na.rm = TRUE)
# RT_proper_go = mean(ifelse(ShowStim.RT >= 200 & ShowStim.RESP == 4 & (StimTag == 'neuG' | StimTag == 'negG'), ShowStim.RT, NA), na.rm = TRUE),
# RT_proper_nogo = mean(ifelse(ShowStim.RT >= 200 & ShowStim.RESP %in% 1:6 & (StimTag == 'neuN' | StimTag == 'negN'), ShowStim.RT, NA), na.rm = TRUE),
# Calculate reaction time for ALL including premature responses
# RT_all_go = mean(ifelse((StimTag == 'neuG' | StimTag == 'negG') & ShowStim.RT != 0, ShowStim.RT, NA), na.rm = TRUE),
# RT_all_nogo = mean(ifelse((StimTag == 'neuN' | StimTag == 'negN') & ShowStim.RT != 0, ShowStim.RT, NA), na.rm = TRUE)
)## `summarise()` has grouped output by 'Subject', 'Condition'. You can override
## using the `.groups` argument.See how many kids have missing accuracy data and manually add them:
ADD THIS KID MANUALLY _ ACCURACY TAKEN FROM NETSTATION:
# Get unique Subject IDs in the ZOO dataset
zoo_subjects <- unique(FULL$Subject)
# Get unique Subject IDs in the accuracy_proportions dataset
acc_subjects <- unique(behavioral_measures$Subject)
# Identify Subject IDs present in the ZOO dataset but not in the accuracy_proportions dataset
missing_subjects <- zoo_subjects[!(zoo_subjects %in% acc_subjects)]
# Print the missing Subject IDs
print(missing_subjects)## [1] "RB101619"# Create a new data frame with the additional rows
new_rows <- data.frame(Subject = rep("RB101619", 4),
Condition = c("Go", "Go", "NoGo", "NoGo"),
Emotion = c("Neg", "Neut", "Neg", "Neut"),
accuracy_go_proportion = c(99, 99, NA, NA),
accuracy_nogo_proportion = c(NA, NA, 42.5, 50),
hit_rate = c(.99, .99, NA, NA),
false_alarm_rate = c(NA, NA, .425, .5))
# Append the new rows to the existing dataset
behavioral_measures <- rbind(behavioral_measures, new_rows)
# Make sure newly added Subject has data now
behavioral_measures[behavioral_measures$Subject == "RB101619",]## # A tibble: 4 × 22
## # Groups: Subject, Condition [2]
## Subject Condition Emotion correct_go incorrect_go premature_go total_go
## <chr> <chr> <chr> <int> <int> <int> <int>
## 1 RB101619 Go Neg NA NA NA NA
## 2 RB101619 Go Neut NA NA NA NA
## 3 RB101619 NoGo Neg NA NA NA NA
## 4 RB101619 NoGo Neut NA NA NA NA
## # ℹ 15 more variables: accuracy_go_proportion <dbl>, hit_rate <dbl>,
## # premature_go_proportion <dbl>, correct_nogo <int>, incorrect_nogo <int>,
## # premature_nogo <int>, total_nogo <int>, accuracy_nogo_proportion <dbl>,
## # false_alarm_proportion <dbl>, false_alarm_rate <dbl>,
## # premature_nogo_proportion <dbl>, RT_proper_go <dbl>, RT_proper_nogo <dbl>,
## # RT_premature_go <dbl>, RT_premature_nogo <dbl>Combine accuracy go and accuracy nogo together, premature go and nogo together, and RT go and RT nogo together:
Since ‘accuracy_go’ and ‘accuracy_nogo’ do not overlap (one of them
is always NA for a given row), you can use the coalesce
function to pick the non-NA value
behavioral_measures <- behavioral_measures %>%
mutate(
accuracy = coalesce(accuracy_go_proportion, accuracy_nogo_proportion),
hit_falsealarm = coalesce(hit_rate, false_alarm_rate),
premature_responses = coalesce(premature_go_proportion, premature_nogo_proportion),
RT_proper = coalesce(RT_proper_go, RT_proper_nogo),
RT_premature = coalesce(RT_premature_go, RT_premature_nogo))
# Calculate z_hit and z_false_alarm and then calculate sensitivity d'- d prime
# Calculate z-scores for hit rate and false alarm rate using the invNorm function
behavioral_measures$hit_rate <- ifelse(behavioral_measures$hit_rate == 1, 0.9999999, behavioral_measures$hit_rate)
behavioral_measures$false_alarm_rate <- ifelse(behavioral_measures$false_alarm_rate == 0, 0.0000001, behavioral_measures$false_alarm_rate)
behavioral_measures <- behavioral_measures %>%
group_by(Subject, Emotion) %>%
mutate(
z_hit = qnorm(hit_rate),
z_false_alarm = qnorm(false_alarm_rate),
sensitivity = (z_hit[Condition == "Go"] - z_false_alarm[Condition == "NoGo"])
) %>%
ungroup()
# Subset to main variables only
eprime <- subset(behavioral_measures, select=c('Subject', "Condition", "Emotion", "accuracy", "hit_falsealarm", "premature_responses", "RT_proper", "RT_premature", "sensitivity"))
head(eprime)## # A tibble: 6 × 9
## Subject Condition Emotion accuracy hit_falsealarm premature_responses
## <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 AE050318 Go Neg 75.8 0.758 9.90
## 2 AE050318 Go Neut 90 0.9 0.917
## 3 AE050318 NoGo Neg 57.5 0.325 23.5
## 4 AE050318 NoGo Neut 70 0.25 16.7
## 5 AH101121 Go Neg 99.2 0.992 0
## 6 AH101121 Go Neut 100 1 0
## # ℹ 3 more variables: RT_proper <dbl>, RT_premature <dbl>, sensitivity <dbl>COMBINE FULL WITH EPRIME
# COMBINE ALL!!
ZOO <- merge(FULL, eprime, by=c("Subject", "Condition", "Emotion"), all.x = TRUE)
head(ZOO)## Subject Condition Emotion Trial Type Laterality Electrode MeanAmp_P2
## 1 AE050318 Go Neg NegGo Left NA -8.274136
## 2 AE050318 Go Neg NegGo Midline NA -8.656789
## 3 AE050318 Go Neg NegGo Right NA -10.997870
## 4 AE050318 Go Neut NeutGo Left NA -8.061495
## 5 AE050318 Go Neut NeutGo Midline NA -11.009666
## 6 AE050318 Go Neut NeutGo Right NA -10.983267
## MeanAmp_N2 MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3
## 1 -12.45291 7.0060606 200 -6.982536 368 -17.04337 512
## 2 -17.64050 4.3699974 212 -6.242593 352 -23.05866 612
## 3 -17.06130 -1.0128204 208 -8.224361 352 -20.62662 576
## 4 -12.32419 6.1267276 212 -6.498847 388 -14.78690 516
## 5 -18.54445 4.2534250 220 -8.465257 384 -22.61550 592
## 6 -17.92275 -0.6663896 220 -9.281374 384 -21.63784 488
## PeakAmp_P3 MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve calculator_gender_cve
## 1 9.8622230 -4.178779 -10.060831 38.1 0
## 2 6.0376027 -8.983711 -16.816070 38.1 0
## 3 0.8441270 -6.063426 -12.402263 38.1 0
## 4 8.5292437 -4.262696 -8.288049 38.1 0
## 5 6.6106956 -7.534787 -14.150243 38.1 0
## 6 0.3001207 -6.939481 -12.356470 38.1 0
## race ethnicity calculator_talkergroup_parent tso_calculated
## 1 2 0 1 1.9
## 2 2 0 1 1.9
## 3 2 0 1 1.9
## 4 2 0 1 1.9
## 5 2 0 1 1.9
## 6 2 0 1 1.9
## disfluency_sldper100words ssi_total disfluency_sldper100words_final
## 1 12 23 12
## 2 12 23 12
## 3 12 23 12
## 4 12 23 12
## 5 12 23 12
## 6 12 23 12
## talkergroup_final gfta_standard ppvt_standard evt_standard teld_rec_standard
## 1 1 121 126 123 146
## 2 1 121 126 123 146
## 3 1 121 126 123 146
## 4 1 121 126 123 146
## 5 1 121 126 123 146
## 6 1 121 126 123 146
## teld_exp_standard teld_spokenlang_standard tocs_1_total tocs_2_total
## 1 135 149 22 6
## 2 135 149 22 6
## 3 135 149 22 6
## 4 135 149 22 6
## 5 135 149 22 6
## 6 135 149 22 6
## tcs_total eprime_condorder_zootask cve_comments comments_tasks handedness_zoo
## 1 25 1 NA
## 2 25 1 NA
## 3 25 1 NA
## 4 25 1 NA
## 5 25 1 NA
## 6 25 1 NA
## accuracy hit_falsealarm premature_responses RT_proper RT_premature
## 1 75.83333 0.7583333 9.9009901 774.044 87.1
## 2 75.83333 0.7583333 9.9009901 774.044 87.1
## 3 75.83333 0.7583333 9.9009901 774.044 87.1
## 4 90.00000 0.9000000 0.9174312 802.213 151.0
## 5 90.00000 0.9000000 0.9174312 802.213 151.0
## 6 90.00000 0.9000000 0.9174312 802.213 151.0
## sensitivity
## 1 1.154714
## 2 1.154714
## 3 1.154714
## 4 1.956041
## 5 1.956041
## 6 1.956041LOAD and merge with trail numbers DF
# Load the file:
# trialnum <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/Zoo_trialnumber_used_10.03.23.csv')
# convert the dataset to long format:
# id.vars argument specifies which columns in the original data frame should remain as they are without being transformed.
# measure.vars argument specifies which columns in the original data frame should be melted
# The variable.name argument specifies the name of the new column that will store the variable names from the measure.vars
# The value.name argument specifies the name of the new column that will store the values from the measure.vars.
# trialnum_long <- melt(trialnum, id.vars = c("Subject"),
# measure.vars = c("NeutNoGo","NeutGo",
# "NegNoGo", "NegGo"),
# variable.name = "StimTag",
# value.name ="TrialNum")
# ZOO <- merge(ZOO, trialnum_long, by=c("Subject", "Condition", "Emotion"), all.x = TRUE)#COMPUTE AND ADD BRIEF SCORES
INHIBIT: This scale assesses inhibitory control and impulsivity. Higher scores indicate greater difficulty in resisting impulses and stopping one’s behavior at the appropriate time.
SHIFT: The Shift scale assesses the ability to move freely from one situation, activity, or aspect of a problem to another as the circumstances demand. It includes aspects such as making transitions, tolerating change, problem-solving flexibly, switching attention, and changing focus.The ability to shift attention and adapt to changing demands is relevant to tasks like the Go NoGo task, and a correlation may be observed between the Shift scale and behavioral accuracy.
emotionalCntrl: The Emotional Control scale measures the impact of executive function problems on emotional expression and assesses a child’s ability to modulate or control emotional responses.Emotional control is relevant to behavioral accuracy, especially in tasks that may evoke emotional responses. This scale could also be relevant to N2 and P3 components, as these components are associated with cognitive and attentional processes.
# Load the file:
brief <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/Brief.csv')
# Drop all rows that has an NA for the first three items (assuming the rest is empty too)
brief <- brief[complete.cases(brief[, c("brief1", "brief2", "brief3")]), ]
# replace individual NA values with 2 (the average score)
brief <- replace(brief, is.na(brief), 2)
# BRIEF PRESCHOOL AGED:
brief <- brief %>%
mutate(inhibit = brief3 + brief8 + brief13 + brief18 + brief23 + brief28 + brief33 + brief38 + brief43 + brief48 + brief52 + brief54 + brief56 + brief58 + brief60 + brief62,
shift = brief5 + brief10 + brief15 + brief20 + brief25 + brief30 + brief35 + brief40 + brief45 + brief50,
emotionalCntrl = brief1 + brief6 + brief11 + brief16 + brief21 + brief26 + brief31 + brief36 + brief41 + brief46,
workingMemory = brief2 + brief7 + brief12 + brief17 + brief22 + brief27 + brief32 + brief37 + brief42 + brief47 + brief51 + brief53 + brief55 + brief57 + brief59 + brief61 + brief63,
planOrganize = brief4 + brief9 + brief14 + brief19 + brief24 + brief29 + brief34 + brief39 + brief44 + brief49,
InhibitorySelfControlIndex_ISCI = inhibit + emotionalCntrl,
FlexibilityIndex_FI = shift + emotionalCntrl,
BehavioralRegulationIndex_BRI = inhibit + shift + emotionalCntrl,
MetacognitionIndex_MI = workingMemory + planOrganize,
GlobalExecutiveComposite_GEC = BehavioralRegulationIndex_BRI + MetacognitionIndex_MI) %>%
dplyr::select(part_id_status, redcap_event_name, inhibit:GlobalExecutiveComposite_GEC) # with select specify it is dplyr, because it conflicts with other packages.
# Create the Subject code with time stamp
brief <- brief %>%
mutate(
Subject = case_when(grepl("t1_arm_1", redcap_event_name) ~ paste0(part_id_status),
grepl("t2_arm_1", redcap_event_name) ~ paste0(part_id_status, "T2"),
grepl("t3_arm_1", redcap_event_name) ~ paste0(part_id_status, "T3"),
grepl("t4_arm_1", redcap_event_name) ~ paste0(part_id_status, "T3"),
TRUE ~ NA_character_)
)
# BRIEF SCHOOL AGED:
# Load the file:
brief_s <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/Brief_s.csv')
# Drop all rows that has an NA for the first three items (assuming the rest is empty too)
brief_s <- brief_s[complete.cases(brief_s[, c("brief1s", "brief2s", "brief3s")]), ]
# replace individual NA values with 2 (the average score)
brief_s <- replace(brief_s, is.na(brief_s), 2)
brief_s <- brief_s %>%
mutate(inhibit = brief38s + brief41s + brief43s + brief44s + brief49s + brief54s + brief55_s + brief56s + brief59s + brief65s,
shift = brief5s + brief6s + brief8s + brief12s + brief13s + brief23s + brief30s + brief39s,
emotionalCntrl = brief1s + brief7s + brief20s + brief25s + brief26s + brief45s + brief50s + brief62s + brief64s + brief70s,
initiate = brief3s + brief10s + brief16s + brief47s + brief48s + brief61s + brief66s + brief71s,
workingMemory = briefs11s + brief15s + brief18s + brief22s + brief28s + brief35s + brief36s + brief40s + brief46s + brief51s + brief53s + brief58s,
planOrganize = brief4s + brief9s + brief14s + brief19s + brief24s + brief29s + brief34s + brief39s + brief44s + brief49s,
orgofMaterials = brief4s + brief29s + brief67s + brief68s + brief69s + brief72s,
monitor = brief14s + brief21s + brief31s + brief34s + brief42s + brief52s + brief60s + brief63_s,
# composite scales:
BehavioralRegulationIndex_BRI = inhibit + shift + emotionalCntrl,
MetacognitionIndex_MI = initiate + workingMemory + planOrganize + orgofMaterials + monitor,
GlobalExecutiveComposite_GEC = BehavioralRegulationIndex_BRI + MetacognitionIndex_MI) %>%
dplyr::select(part_id_status, redcap_event_name, inhibit:GlobalExecutiveComposite_GEC)
# Create the Subject code with time stamp
brief_s <- brief_s %>%
mutate(
Subject = case_when(grepl("t1_arm_1", redcap_event_name) ~ paste0(part_id_status),
grepl("t2_arm_1", redcap_event_name) ~ paste0(part_id_status, "T2"),
grepl("t3_arm_1", redcap_event_name) ~ paste0(part_id_status, "T3"),
grepl("t4_arm_1", redcap_event_name) ~ paste0(part_id_status, "T4"),
TRUE ~ NA_character_)
)
# Merge them together
# In the by section list all common variable names:
brief_merged <- merge(brief, brief_s, by = c("Subject", "part_id_status", "redcap_event_name", "inhibit", "shift", "emotionalCntrl", "workingMemory", "planOrganize", "BehavioralRegulationIndex_BRI", "MetacognitionIndex_MI", "GlobalExecutiveComposite_GEC", "inhibit", "shift", "inhibit", "shift"), all = TRUE)
# Merge with ZOO
ZOO <- merge(ZOO, brief_merged, by=c("Subject"), all.x = TRUE)
# 2 KIDS DATA IS MISSING
# unique(ZOO$Subject[is.na(ZOO$inhibit)])
# [1] "GB012717" "OG013016"#COMPUTE AND ADD CBQ SCORES
# Load the file:
cbq <- read.csv(file = '/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CBQ.csv')
# Calculate percentage of NA values in each row
# na_percent <- rowSums(is.na(cbq))/ncol(cbq)
# Subset rows with less than 70% NA values
# cbq <- cbq[na_percent < 0.7, ]
# Define a function to calculate r values
subtract_eight <- function(x) {
8 - x
}
# Apply the function to columns cbq0001 through cbq0195
cbq <- cbq %>%
mutate(across(matches("^cbq0[01][0-9][0-9]$"), subtract_eight, .names = "{col}r"))
#Calculate the sub scales - create new variables
cbq <- cbq %>%
mutate(
act = rowMeans(dplyr::select(., cbq0001, cbq0025, cbq0041r, cbq0048, cbq0088r, cbq0102r, cbq0123r, cbq0126r, cbq0145r, cbq0152, cbq0172, cbq0187, cbq0192r), na.rm = TRUE),
fru = rowMeans(dplyr::select(., cbq0002, cbq0019r, cbq0034, cbq0062, cbq0073, cbq0078, cbq0120r, cbq0128, cbq0140, cbq0156r, cbq0173, cbq0181, cbq0193), na.rm = TRUE),
app = rowMeans(dplyr::select(., cbq0010, cbq0024, cbq0035, cbq0069, cbq0082, cbq0096, cbq0117, cbq0131r, cbq0148, cbq0166, cbq0175r, cbq0188r, cbq0191r), na.rm = TRUE),
attfo = rowMeans(dplyr::select(., cbq0016, cbq0038r, cbq0047r, cbq0125, cbq0144, cbq0160, cbq0171r, cbq0186, cbq0195r), na.rm = TRUE),
attshi = rowMeans(dplyr::select(., cbq0006r, cbq0029, cbq0095r, cbq0180, cbq0184r), na.rm = TRUE),
dis = rowMeans(dplyr::select(., cbq0005r, cbq0021, cbq0061, cbq0087, cbq0097, cbq0101r, cbq0115, cbq0132, cbq0141, cbq0157, cbq0178, cbq0190r), na.rm = TRUE),
sth = rowMeans(dplyr::select(., cbq0014r, cbq0027, cbq0042, cbq0053r, cbq0068r, cbq0085, cbq0092, cbq0103, cbq0118, cbq0134, cbq0150r, cbq0167r, cbq0177), na.rm = TRUE),
fea = rowMeans(dplyr::select(., cbq0015r, cbq0040, cbq0050, cbq0058r, cbq0070r, cbq0080, cbq0091, cbq0130, cbq0138r, cbq0161r, cbq0176r, cbq0189), na.rm = TRUE),
hip = rowMeans(dplyr::select(., cbq0008, cbq0022, cbq0030r, cbq0051r, cbq0060r, cbq0067, cbq0077, cbq0100, cbq0107, cbq0124, cbq0139, cbq0159r, cbq0182), na.rm = TRUE),
imp = rowMeans(dplyr::select(., cbq0013, cbq0026, cbq0046, cbq0059, cbq0071r, cbq0079r, cbq0090r, cbq0104, cbq0114, cbq0137r, cbq0155, cbq0169r, cbq0183r), na.rm = TRUE),
inh = rowMeans(dplyr::select(., cbq0004, cbq0020, cbq0032r, cbq0063, cbq0075, cbq0093r, cbq0108r, cbq0116, cbq0136, cbq0147, cbq0162r, cbq0168, cbq0185), na.rm = TRUE),
lip = rowMeans(dplyr::select(., cbq0012r, cbq0036, cbq0054, cbq0066r, cbq0076,cbq0111r, cbq0113, cbq0133, cbq0146, cbq0151,cbq0164, cbq0174), na.rm = TRUE),
per = rowMeans(dplyr::select(., cbq0009, cbq0028r, cbq0031, cbq0052, cbq0065,cbq0084r, cbq0098, cbq0105, cbq0122r, cbq0142r,cbq0154, cbq0170r), na.rm = TRUE),
sad = rowMeans(dplyr::select(., cbq0018, cbq0039, cbq0044, cbq0055, cbq0064,cbq0072r, cbq0081, cbq0094, cbq0109r, cbq0112r,cbq0127, cbq0149r), na.rm = TRUE),
shy = rowMeans(dplyr::select(., cbq0007, cbq0017r, cbq0023r, cbq0037,cbq0045r, cbq0057r, cbq0074, cbq0089, cbq0106,cbq0119r, cbq0129r, cbq0143, cbq0158r), na.rm = TRUE),
smi =rowMeans(dplyr::select(., cbq0011, cbq0043r, cbq0056, cbq0083r,cbq0099r, cbq0110, cbq0121r, cbq0135r, cbq0152,cbq0163, cbq0165r, cbq0179, cbq0194), na.rm = TRUE)
)
cbq <- cbq %>%
mutate(shy_r=(8-shy)) %>%
mutate(sth_r=(8-sth)) %>%
rename(activity_level = act) %>%
rename(anger_frustration = fru) %>%
rename(approach = app) %>%
rename(attention_focus = attfo) %>%
rename(attention_shift = attshi) %>%
rename(discomfort = dis) %>%
rename(soothability = sth) %>%
rename(fear = fea) %>%
rename(hi_intense_pleas = hip) %>%
rename(impulsivity = imp) %>%
rename(inhibit_control = inh) %>%
rename(lo_instense_pleas = lip) %>%
rename(percept_sensitive = per) %>%
rename(sadness = sad) %>%
rename(shyness = shy) %>%
rename(smiling_laughter = smi)
cbq <- cbq %>%
mutate(surgency = rowMeans(dplyr::select(., activity_level,hi_intense_pleas, impulsivity, shy_r)),
effortful_con = rowMeans(dplyr::select(., attention_focus, inhibit_control,lo_instense_pleas, percept_sensitive)),
neg_affect = rowMeans(dplyr::select(., anger_frustration, discomfort,fear, sadness, sth_r))) %>%
dplyr::select(part_id_status, redcap_event_name, activity_level:neg_affect)
# Create the Subject code with time stamp
cbq <- cbq %>%
mutate(
Subject = case_when(grepl("t1_arm_1", redcap_event_name) ~ paste0(part_id_status),
grepl("t2_arm_1", redcap_event_name) ~ paste0(part_id_status, "T2"),
grepl("t3_arm_1", redcap_event_name) ~ paste0(part_id_status, "T3"),
TRUE ~ NA_character_)
)
# Merge with ZOO
ZOO <- merge(ZOO, cbq, by=c("Subject"), all.x = TRUE)WRITE THE FINAL DATAFRAME INTO CSV AND SAVE TO LOCAL DRIVE:
# WRITE THE FINAL DATAFRAME INTO CSV AND SAVE TO LOCAL DRIVE:
write.csv(ZOO, "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/ZOO.csv")
head(ZOO)## Subject Condition Emotion Trial Type Laterality Electrode MeanAmp_P2
## 1 AE050318 Go Neg NegGo Midline NA -8.6567894
## 2 AE050318 Go Neut NeutGo Left NA -8.0614951
## 3 AE050318 Go Neg NegGo Left NA -8.2741357
## 4 AE050318 NoGo Neg NegNoGo Right NA 3.0738593
## 5 AE050318 Go Neg NegGo Right NA -10.9978696
## 6 AE050318 NoGo Neg NegNoGo Midline NA 0.1368089
## MeanAmp_N2 MeanAmp_P3 Latency_P2 PeakAmp_P2 Latency_N2 PeakAmp_N2 Latency_P3
## 1 -17.6405009 4.369997 212 -6.242593 352 -23.058663 612
## 2 -12.3241910 6.126728 212 -6.498847 388 -14.786896 516
## 3 -12.4529149 7.006061 200 -6.982536 368 -17.043367 512
## 4 -0.6563204 0.449258 232 5.512485 400 -3.018455 508
## 5 -17.0612959 -1.012820 208 -8.224361 352 -20.626624 576
## 6 -2.7636031 3.345500 252 3.300071 452 -5.257907 600
## PeakAmp_P3 MeanAmp_N2P2 PeakAmp_N2P2 calculator_age_cve calculator_gender_cve
## 1 6.037603 -8.983711 -16.816070 38.1 0
## 2 8.529244 -4.262696 -8.288049 38.1 0
## 3 9.862223 -4.178779 -10.060831 38.1 0
## 4 3.116096 -3.730180 -8.530940 38.1 0
## 5 0.844127 -6.063426 -12.402263 38.1 0
## 6 6.301873 -2.900412 -8.557978 38.1 0
## race ethnicity calculator_talkergroup_parent tso_calculated
## 1 2 0 1 1.9
## 2 2 0 1 1.9
## 3 2 0 1 1.9
## 4 2 0 1 1.9
## 5 2 0 1 1.9
## 6 2 0 1 1.9
## disfluency_sldper100words ssi_total disfluency_sldper100words_final
## 1 12 23 12
## 2 12 23 12
## 3 12 23 12
## 4 12 23 12
## 5 12 23 12
## 6 12 23 12
## talkergroup_final gfta_standard ppvt_standard evt_standard teld_rec_standard
## 1 1 121 126 123 146
## 2 1 121 126 123 146
## 3 1 121 126 123 146
## 4 1 121 126 123 146
## 5 1 121 126 123 146
## 6 1 121 126 123 146
## teld_exp_standard teld_spokenlang_standard tocs_1_total tocs_2_total
## 1 135 149 22 6
## 2 135 149 22 6
## 3 135 149 22 6
## 4 135 149 22 6
## 5 135 149 22 6
## 6 135 149 22 6
## tcs_total eprime_condorder_zootask cve_comments comments_tasks handedness_zoo
## 1 25 1 NA
## 2 25 1 NA
## 3 25 1 NA
## 4 25 1 NA
## 5 25 1 NA
## 6 25 1 NA
## accuracy hit_falsealarm premature_responses RT_proper RT_premature
## 1 75.83333 0.7583333 9.9009901 774.044 87.10
## 2 90.00000 0.9000000 0.9174312 802.213 151.00
## 3 75.83333 0.7583333 9.9009901 774.044 87.10
## 4 57.50000 0.3250000 23.5294118 566.000 111.75
## 5 75.83333 0.7583333 9.9009901 774.044 87.10
## 6 57.50000 0.3250000 23.5294118 566.000 111.75
## sensitivity part_id_status.x redcap_event_name.x inhibit shift emotionalCntrl
## 1 1.154714 AE050318 t1_arm_1 36 15 13
## 2 1.956041 AE050318 t1_arm_1 36 15 13
## 3 1.154714 AE050318 t1_arm_1 36 15 13
## 4 1.154714 AE050318 t1_arm_1 36 15 13
## 5 1.154714 AE050318 t1_arm_1 36 15 13
## 6 1.154714 AE050318 t1_arm_1 36 15 13
## workingMemory planOrganize BehavioralRegulationIndex_BRI
## 1 37 20 64
## 2 37 20 64
## 3 37 20 64
## 4 37 20 64
## 5 37 20 64
## 6 37 20 64
## MetacognitionIndex_MI GlobalExecutiveComposite_GEC
## 1 57 121
## 2 57 121
## 3 57 121
## 4 57 121
## 5 57 121
## 6 57 121
## InhibitorySelfControlIndex_ISCI FlexibilityIndex_FI initiate orgofMaterials
## 1 49 28 NA NA
## 2 49 28 NA NA
## 3 49 28 NA NA
## 4 49 28 NA NA
## 5 49 28 NA NA
## 6 49 28 NA NA
## monitor part_id_status.y redcap_event_name.y activity_level anger_frustration
## 1 NA AE050318 t1_arm_1 5.307692 3.923077
## 2 NA AE050318 t1_arm_1 5.307692 3.923077
## 3 NA AE050318 t1_arm_1 5.307692 3.923077
## 4 NA AE050318 t1_arm_1 5.307692 3.923077
## 5 NA AE050318 t1_arm_1 5.307692 3.923077
## 6 NA AE050318 t1_arm_1 5.307692 3.923077
## approach attention_focus attention_shift discomfort soothability fear
## 1 3.846154 2.666667 5.2 3.416667 5.307692 3.666667
## 2 3.846154 2.666667 5.2 3.416667 5.307692 3.666667
## 3 3.846154 2.666667 5.2 3.416667 5.307692 3.666667
## 4 3.846154 2.666667 5.2 3.416667 5.307692 3.666667
## 5 3.846154 2.666667 5.2 3.416667 5.307692 3.666667
## 6 3.846154 2.666667 5.2 3.416667 5.307692 3.666667
## hi_intense_pleas impulsivity inhibit_control lo_instense_pleas
## 1 5.307692 4.307692 4 5.166667
## 2 5.307692 4.307692 4 5.166667
## 3 5.307692 4.307692 4 5.166667
## 4 5.307692 4.307692 4 5.166667
## 5 5.307692 4.307692 4 5.166667
## 6 5.307692 4.307692 4 5.166667
## percept_sensitive sadness shyness smiling_laughter shy_r sth_r
## 1 4.666667 2.916667 3.692308 6.307692 4.307692 2.692308
## 2 4.666667 2.916667 3.692308 6.307692 4.307692 2.692308
## 3 4.666667 2.916667 3.692308 6.307692 4.307692 2.692308
## 4 4.666667 2.916667 3.692308 6.307692 4.307692 2.692308
## 5 4.666667 2.916667 3.692308 6.307692 4.307692 2.692308
## 6 4.666667 2.916667 3.692308 6.307692 4.307692 2.692308
## surgency effortful_con neg_affect
## 1 4.807692 4.125 3.323077
## 2 4.807692 4.125 3.323077
## 3 4.807692 4.125 3.323077
## 4 4.807692 4.125 3.323077
## 5 4.807692 4.125 3.323077
## 6 4.807692 4.125 3.323077REMOVE THESE KIDS BASED ON LOWER ACCURACY + NEGATIVE EEG COMMENTS:
ZOO_bad_subjects <- ZOO %>%
group_by(Subject) %>%
filter(all(accuracy[`Trial Type` == "NeutNoGo"] <= 40) |
all(accuracy[`Trial Type` == "NegNoGo"] <= 40) |
all(accuracy[`Trial Type` == "NeutGo"] <= 70) |
all(accuracy[`Trial Type` == "NegGo"] <= 70))
bad_subjects = unique(ZOO_bad_subjects$Subject)
bad_subjects## [1] "EG030618" "HH061919" "JS121321" "NL041119" "RT032219" "WS051018"# READ THE COMMENTS FOR THOSE LOW ACCURACY KIDS:
ZOO_filtered <- dplyr::filter(ZOO, Subject %in% bad_subjects)
ZOO_filtered <- subset(ZOO_filtered, select=c('Subject','talkergroup_final','Trial Type','accuracy', 'calculator_age_cve','RT_proper', 'cve_comments','comments_tasks','handedness_zoo'))
ZOO_filtered <- ZOO_filtered %>% distinct() # Use distinct function to remove duplicates
print(ZOO_filtered)## Subject talkergroup_final Trial Type accuracy calculator_age_cve RT_proper
## 1 EG030618 1 NegGo 73.94958 48.8 830.8182
## 2 EG030618 1 NeutGo 84.16667 48.8 854.0990
## 3 EG030618 1 NegNoGo 30.00000 48.8 894.8333
## 4 EG030618 1 NeutNoGo 72.50000 48.8 803.7273
## 5 HH061919 1 NeutGo 95.00000 76.4 556.8509
## 6 HH061919 1 NegGo 80.50847 76.4 655.8947
## 7 HH061919 1 NegNoGo 60.00000 76.4 666.4000
## 8 HH061919 1 NeutNoGo 35.00000 76.4 467.3200
## 9 JS121321 1 NegGo 69.49153 41.5 963.2683
## 10 JS121321 1 NeutNoGo 72.50000 41.5 1088.6250
## 11 JS121321 1 NegNoGo 85.00000 41.5 1112.6667
## 12 JS121321 1 NeutGo 84.16667 41.5 873.1287
## 13 NL041119 1 NegNoGo 75.00000 41.8 693.3000
## 14 NL041119 1 NeutNoGo 45.00000 41.8 891.6818
## 15 NL041119 1 NegGo 80.83333 41.8 920.0206
## 16 NL041119 1 NeutGo 62.50000 41.8 844.6267
## 17 RT032219 0 NegGo 78.33333 38.0 936.8191
## 18 RT032219 0 NeutGo 64.16667 38.0 913.0519
## 19 RT032219 0 NegNoGo 62.50000 38.0 622.0714
## 20 RT032219 0 NeutNoGo 45.00000 38.0 958.1905
## 21 WS051018 1 NegGo 85.00000 77.3 537.2451
## 22 WS051018 1 NeutGo 94.95798 77.3 556.3451
## 23 WS051018 1 NegNoGo 40.00000 77.3 412.8571
## 24 WS051018 1 NeutNoGo 52.50000 77.3 491.0000
## cve_comments
## 1 No emotional regulation, no rhythm task, no reaction time
## 2 No emotional regulation, no rhythm task, no reaction time
## 3 No emotional regulation, no rhythm task, no reaction time
## 4 No emotional regulation, no rhythm task, no reaction time
## 5 Performed TELD B to see if he scored above our cut off.
## 6 Performed TELD B to see if he scored above our cut off.
## 7 Performed TELD B to see if he scored above our cut off.
## 8 Performed TELD B to see if he scored above our cut off.
## 9 Another speech sample was taken since he was borderline last time.
## 10 Another speech sample was taken since he was borderline last time.
## 11 Another speech sample was taken since he was borderline last time.
## 12 Another speech sample was taken since he was borderline last time.
## 13
## 14
## 15
## 16
## 17
## 18
## 19
## 20
## 21 The child had difficulty attending to most tasks including the NIH toolbox. Fidgeting and changing hands to response frequently. He had hard time sitting still. With the EEg cap he was restless and moving frequently as well.
## 22 The child had difficulty attending to most tasks including the NIH toolbox. Fidgeting and changing hands to response frequently. He had hard time sitting still. With the EEg cap he was restless and moving frequently as well.
## 23 The child had difficulty attending to most tasks including the NIH toolbox. Fidgeting and changing hands to response frequently. He had hard time sitting still. With the EEg cap he was restless and moving frequently as well.
## 24 The child had difficulty attending to most tasks including the NIH toolbox. Fidgeting and changing hands to response frequently. He had hard time sitting still. With the EEg cap he was restless and moving frequently as well.
## comments_tasks
## 1
## 2
## 3
## 4
## 5 During the Zoo Task, he required a lot of reminders to not push the button for the helpers. During the second block (affective) of the Zoo Task, he said he was very tired and refused to push the button for about 10-20 trials- required a lot of redirection to keep going. During the Reactivity Task, the second block(unpleasant) he moved a lot and talked a lot- required a lot of redirection to keep going and the skin conductance electrode fell off for part of that block. RESTING STATE and ZOO TASK are in one file.
## 6 During the Zoo Task, he required a lot of reminders to not push the button for the helpers. During the second block (affective) of the Zoo Task, he said he was very tired and refused to push the button for about 10-20 trials- required a lot of redirection to keep going. During the Reactivity Task, the second block(unpleasant) he moved a lot and talked a lot- required a lot of redirection to keep going and the skin conductance electrode fell off for part of that block. RESTING STATE and ZOO TASK are in one file.
## 7 During the Zoo Task, he required a lot of reminders to not push the button for the helpers. During the second block (affective) of the Zoo Task, he said he was very tired and refused to push the button for about 10-20 trials- required a lot of redirection to keep going. During the Reactivity Task, the second block(unpleasant) he moved a lot and talked a lot- required a lot of redirection to keep going and the skin conductance electrode fell off for part of that block. RESTING STATE and ZOO TASK are in one file.
## 8 During the Zoo Task, he required a lot of reminders to not push the button for the helpers. During the second block (affective) of the Zoo Task, he said he was very tired and refused to push the button for about 10-20 trials- required a lot of redirection to keep going. During the Reactivity Task, the second block(unpleasant) he moved a lot and talked a lot- required a lot of redirection to keep going and the skin conductance electrode fell off for part of that block. RESTING STATE and ZOO TASK are in one file.
## 9 It took him a while to get the task during zoo but he got it towards the 2nd quarter. Good attention in general.
## 10 It took him a while to get the task during zoo but he got it towards the 2nd quarter. Good attention in general.
## 11 It took him a while to get the task during zoo but he got it towards the 2nd quarter. Good attention in general.
## 12 It took him a while to get the task during zoo but he got it towards the 2nd quarter. Good attention in general.
## 13 DO NOT use Reactivity data. Child was rarely looking at the screen. She was talking to mom and putting her head down to rest on the table.
## 14 DO NOT use Reactivity data. Child was rarely looking at the screen. She was talking to mom and putting her head down to rest on the table.
## 15 DO NOT use Reactivity data. Child was rarely looking at the screen. She was talking to mom and putting her head down to rest on the table.
## 16 DO NOT use Reactivity data. Child was rarely looking at the screen. She was talking to mom and putting her head down to rest on the table.
## 17 RESTING: mom was talking throughout to help him attend (according to her) ZOO TASK: Concentration and motivation varied REACTIVITY: Task was started before Acqknowledge was running. Consequently, the physio for the first several pictures was not recorded.
## 18 RESTING: mom was talking throughout to help him attend (according to her) ZOO TASK: Concentration and motivation varied REACTIVITY: Task was started before Acqknowledge was running. Consequently, the physio for the first several pictures was not recorded.
## 19 RESTING: mom was talking throughout to help him attend (according to her) ZOO TASK: Concentration and motivation varied REACTIVITY: Task was started before Acqknowledge was running. Consequently, the physio for the first several pictures was not recorded.
## 20 RESTING: mom was talking throughout to help him attend (according to her) ZOO TASK: Concentration and motivation varied REACTIVITY: Task was started before Acqknowledge was running. Consequently, the physio for the first several pictures was not recorded.
## 21
## 22
## 23
## 24
## handedness_zoo
## 1 NA
## 2 NA
## 3 NA
## 4 NA
## 5 1
## 6 1
## 7 1
## 8 1
## 9 1
## 10 1
## 11 1
## 12 1
## 13 1
## 14 1
## 15 1
## 16 1
## 17 1
## 18 1
## 19 1
## 20 1
## 21 NA
## 22 NA
## 23 NA
## 24 NAREMOVE THESE KIDS with <30% accuracy in Nogo, and <60% in Go.
subjects_to_remove <- c("EG030618","HH061919","WS051018")
ZOO_good <- ZOO
ZOO_good <- dplyr::filter(ZOO_good, !Subject %in% subjects_to_remove)
write.csv(ZOO_good, "/Users/aysuerdemir/Desktop/R workspace/ERP_Zoo/CrossSectional/Mix/ZOO_good.csv")DRAW N2P2 and P3 WAVEFORM GRAPHS YOURSELF
# Calculate the average of specific columns (as specificed by these electrode numbers below) across all rows and create a new column with these averages
# Combine all conditions together into a single dataframe
# This step is necessary to reverse the unclass() function we have used above.
# We need to create subsets from the original combo dataset first ADN THEN unclass before using m.measures()
combo_new <- rbind.data.frame(neg_go, neg_nogo, neut_go, neut_nogo)
combo_old <- rbind.data.frame(neg_go_old, neg_nogo_old, neut_go_old, neut_nogo_old)
# Add the "talkergroup_final" column from the "FULL" dataset to the "combo" dataset based on the common "Subject" column
combo_with_group_new <- combo_new %>%
mutate(talkergroup_final = FULL$talkergroup_final[match(Subject, FULL$Subject)])
combo_with_group_old <- combo_old %>%
mutate(talkergroup_final = FULL$talkergroup_final[match(Subject, FULL$Subject)])
# REMOVE THESE THREE KIDS FROM THE DATASET:
combo_with_group_new <- dplyr::filter(combo_with_group_new, Subject!="EG030618", Subject!="HH061919", Subject!="WS051018")
# CREATE MEAN N2P2 AND P3 VALUES FOR EACH ROW (TIMEPOINT) USING THE SPECIFIED ELECTRODE NUMBERS
newnet_rawdata <- combo_with_group_new %>%
mutate(
N2P2_waveform = rowMeans(dplyr::select(., all_of(FCz_newnets))),
P3_waveform = rowMeans(dplyr::select(., all_of(Pz_newnets)))
)
oldnet_rawdata <- combo_with_group_old %>%
mutate(
N2P2_waveform = rowMeans(dplyr::select(., all_of(FCz_oldnets))),
P3_waveform = rowMeans(dplyr::select(., all_of(Pz_oldnets)))
)
# Combine old and new net data together:
rawdata <- full_join(newnet_rawdata, oldnet_rawdata)## Joining with `by = join_by(Subject, Stimulus, Time, V1, V2, V3, V4, V5, V6, V7,
## V8, V9, V10, V11, V12, V13, V14, V15, V16, V17, V18, V19, V20, V21, V22, V23,
## V24, V25, V26, V27, V28, V29, V30, V31, V32, V33, V34, V35, V36, V37, V38, V39,
## V40, V41, V42, V43, V44, V45, V46, V47, V48, V49, V50, V51, V52, V53, V54, V55,
## V56, V57, V58, V59, V60, V61, V62, V63, V64, V65, V66, V67, V68, V69, V70, V71,
## V72, V73, V74, V75, V76, V77, V78, V79, V80, V81, V82, V83, V84, V85, V86, V87,
## V88, V89, V90, V91, V92, V93, V94, V95, V96, V97, V98, V99, V100, V101, V102,
## V103, V104, V105, V106, V107, V108, V109, V110, V111, V112, V113, V114, V115,
## V116, V117, V118, V119, V120, V121, V122, V123, V124, V125, V126, V127, V128,
## V129, talkergroup_final, N2P2_waveform, P3_waveform)`# How many kids in each group?
talkergroup_counts <- table(rawdata$talkergroup_final)
print(talkergroup_counts)/(275*4)##
## 0 1
## 46200 40700##
## 0 1
## 42 37# CREATE A SINGLE SUMMARY WAVEFORM FOR N2P2 AND P3 ACROSS ALL PARTICIPANTS IN EACH GROUP
summary_waveforms <-
rawdata %>%
group_by(Stimulus, Time, talkergroup_final) %>%
summarise(Average_N2P2 = mean(N2P2_waveform),Average_P3 = mean(P3_waveform) )## `summarise()` has grouped output by 'Stimulus', 'Time'. You can override using
## the `.groups` argument.# Define the X-axis vertical lines
vertical_lines1 <- c(180, 320, 550) # Specify the X-axis positions
# Define the X-axis vertical lines
vertical_lines2 <- c(400, 750) # Specify the X-axis positions
#Red
line_colors <- c("NeutGo" = "#D6E4F0", "NeutNoGo" = "#0000CD", "NegGo" = "#FFB6B6", "NegNoGo" = "#FF0000")
# line_colors <- c("NeutGo" = "lightskyblue1", "NeutNoGo" = "mediumblue", "NegGo" = "pink1", "NegNoGo" = "red2")
# DRAW THE GRAPHS
CWNS_N2P2_waveform <- ggplot(data = subset(summary_waveforms, talkergroup_final == 0),
aes(x = Time, y = Average_N2P2, color = Stimulus)) +
geom_line(stat = "identity", size = 1.5) +
labs(x = "Time (ms)", y = "Amplitude in Microvolts (μV)") +
scale_x_continuous(limits = c(-100, 900), breaks = seq(-100, 900, 100), position = "top") +
scale_y_continuous(limits = c(-15, 2), breaks = seq(-14, 2, 2)) +
scale_color_manual(values = line_colors, labels = c("Affective Go", "Affective NoGo","Neutral Go", "Neutral NoGo")) + # Set line colors manually
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
plot.background = element_rect(fill = "white"),
axis.line = element_line(linetype = "solid", color = "gray10", size = 1.4),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size = 12),
legend.title =element_blank()
) +
geom_vline(xintercept = vertical_lines1, color = "gray50", linetype = "dashed") +
geom_hline(yintercept = 0, color = "gray70", linetype = "dotted") +
geom_vline(xintercept = 0, color = "gray70", linetype = "dotted") +
ggtitle("CWNS N2P2 Activity") +
theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold", vjust = 2)) +
theme(legend.position = c(.85, .2)) # Set the position of the legend## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.plot(CWNS_N2P2_waveform)## Warning: Removed 96 rows containing missing values (`geom_line()`).
CWS_N2P2_waveform <- ggplot(data = subset(summary_waveforms, talkergroup_final == 1),
aes(x = Time, y = Average_N2P2, color = Stimulus)) +
geom_line(stat = "identity", size = 1.3) +
labs(x = "Time (ms)", y = "Amplitude in Microvolts (μV)") +
scale_x_continuous(limits = c(-100, 900), breaks = seq(-100, 900, 100), position = "top") +
scale_y_continuous(limits = c(-15, 2), breaks = seq(-14, 2, 2)) +
scale_color_manual(values = line_colors, labels = c("Affective Go", "Affective NoGo","Neutral Go", "Neutral NoGo")) + # Set line colors manually
theme(
axis.text = element_text(size = 14 , color = "black"),
axis.title = element_text(size = 14),
plot.background = element_rect(fill = "white"),
axis.line = element_line(linetype = "solid", color = "gray10", size = 1.4),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size = 12),
legend.title =element_blank()
) +
geom_vline(xintercept = vertical_lines1, color = "gray50", linetype = "dashed") +
geom_hline(yintercept = 0, color = "gray70", linetype = "dotted") +
geom_vline(xintercept = 0, color = "gray70", linetype = "dotted") +
ggtitle("CWS N2P2 Activity") +
theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold", vjust = 2)) +
theme(legend.position = c(.85, .2)) # Set the position of the legend
plot(CWS_N2P2_waveform) ## Warning: Removed 108 rows containing missing values (`geom_line()`).
CWNS_P3_waveform <- ggplot(data = subset(summary_waveforms, talkergroup_final == 0),
aes(x = Time, y = Average_P3, color = Stimulus)) +
geom_line(stat = "identity", size = 1.3) +
labs(x = "Time (ms)", y = "Amplitude in Microvolts (μV)") +
scale_x_continuous(limits = c(-100, 900), breaks = seq(-100, 900, 100), position = "top")+
scale_y_continuous(limits = c(-2, 13), breaks = seq(-2, 13, 2)) +
scale_color_manual(values = line_colors, labels = c("Affective Go", "Affective NoGo","Neutral Go", "Neutral NoGo")) + # Set line colors manually
theme(
axis.text = element_text(size = 14, color = "black"),
axis.title = element_text(size = 14),
plot.background = element_rect(fill = "white"),
axis.line = element_line(linetype = "solid", color = "gray10", size = 1.4),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size = 12),
legend.title =element_blank()
) +
geom_vline(xintercept = vertical_lines2, color = "gray50", linetype = "dashed") +
geom_hline(yintercept = 0, color = "gray70", linetype = "dotted") +
geom_vline(xintercept = 0, color = "gray70", linetype = "dotted") +
ggtitle("CWNS P3 Activity") +
theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold", vjust = 2)) +
theme(legend.position = c(.65, .3))
plot(CWNS_P3_waveform)## Warning: Removed 96 rows containing missing values (`geom_line()`).
CWS_P3_waveform <- ggplot(data = subset(summary_waveforms, talkergroup_final == 1),
aes(x = Time, y = Average_P3, color = Stimulus)) +
geom_line(stat = "identity", size = 1.3) +
labs(x = "Time (ms)", y = "Amplitude in Microvolts (μV)") +
scale_x_continuous(limits = c(-100, 900), breaks = seq(-100, 900, 100), position = "top")+
scale_y_continuous(limits = c(-2, 13), breaks = seq(-2, 13, 2)) +
scale_color_manual(values = line_colors, labels = c("Affective Go", "Affective NoGo","Neutral Go", "Neutral NoGo")) + # Set line colors manually
theme(
axis.text = element_text(size = 14 , color = "black"),
axis.title = element_text(size = 14),
plot.background = element_rect(fill = "white"),
axis.line = element_line(linetype = "solid", color = "gray10", size = 1.4),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.text = element_text(size = 12),
legend.title =element_blank()
) +
geom_vline(xintercept = vertical_lines2, color = "gray50", linetype = "dashed") +
geom_hline(yintercept = 0, color = "gray70", linetype = "dotted") +
geom_vline(xintercept = 0, color = "gray70", linetype = "dotted") +
ggtitle("CWN P3 Activity") +
theme(plot.title = element_text(hjust = 0.5, size = 24, face = "bold", vjust = 2)) +
theme(legend.position = c(.65, .3))
plot(CWS_P3_waveform) ## Warning: Removed 96 rows containing missing values (`geom_line()`).